├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── figures ├── figure_13.png ├── figure_14.png ├── figure_15.png ├── figure_16.png ├── figure_17.png └── figure_4.png ├── ge.png └── hyperspectralToolbox ├── contents.m ├── fnnls.m ├── hyperAce.m ├── hyperAmsd.m ├── hyperAtgp.m ├── hyperCem.m ├── hyperConvert2Colormap.m ├── hyperConvert2d.m ├── hyperConvert3d.m ├── hyperConvexHullRemoval.m ├── hyperCorr.m ├── hyperCov.m ├── hyperDemo.m ├── hyperDemo_ASD_reader.m ├── hyperDemo_RIT_data.m ├── hyperDemo_detectors.m ├── hyperDemo_mams_RIT_data.m ├── hyperDestreak.m ├── hyperFcls.m ├── hyperFclsMatlab.m ├── hyperFileFind.m ├── hyperGetEnviSignature.m ├── hyperGetHymapWavelengthsNm.m ├── hyperGlrt.m ├── hyperHfcVd.m ├── hyperHud.m ├── hyperIcaComponentScores.m ├── hyperIcaEea.m ├── hyperImagesc.m ├── hyperImshow.m ├── hyperMatchedFilter.m ├── hyperMax2d.m ├── hyperMnf.m ├── hyperNapc.m ├── hyperNnls.m ├── hyperNormXCorr.m ├── hyperNormalize.m ├── hyperOrthorectify.m ├── hyperOsp.m ├── hyperPct.m ├── hyperPlmf.m ├── hyperPpi.m ├── hyperReadAsd.m ├── hyperReadAvirisRfl.m ├── hyperReadAvirisSpc.m ├── hyperReadSpecpr.m ├── hyperResample.m ├── hyperRmf.m ├── hyperRoc.m ├── hyperRxDetector.m ├── hyperSam.m ├── hyperSaveFigure.m ├── hyperSid.m ├── hyperSignedAce.m ├── hyperUcls.m ├── hyperVca.m └── hyperWhiten.m /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # Windows Installer files 12 | *.cab 13 | *.msi 14 | *.msm 15 | *.msp 16 | 17 | # Windows shortcuts 18 | *.lnk 19 | 20 | # ========================= 21 | # Operating System Files 22 | # ========================= 23 | 24 | # OSX 25 | # ========================= 26 | 27 | .DS_Store 28 | .AppleDouble 29 | .LSOverride 30 | 31 | # Thumbnails 32 | ._* 33 | 34 | # Files that might appear on external disk 35 | .Spotlight-V100 36 | .Trashes 37 | 38 | # Directories potentially created on remote AFP share 39 | .AppleDB 40 | .AppleDesktop 41 | Network Trash Folder 42 | Temporary Items 43 | .apdisk 44 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | 676 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MATLAB Hyperspectral Toolbox 2 | 3 | A comprehensive MATLAB toolbox for hyperspectral image processing and analysis, providing state-of-the-art exploitation algorithms for research and educational purposes. 4 | 5 | ## Overview 6 | 7 | This toolbox was originally developed to support research for my Master's thesis "[An Evaluation of Three Endmember Extraction Algorithms: ATGP, ICA-EEA, and VCA](https://etda.libraries.psu.edu/catalog/8265)" under the advisorship of Dr. Tim Kane at Penn State's Remote Sensing and Space Systems Lab. The work has since evolved into a broader collection of hyperspectral analysis tools that continues to serve the remote sensing research community. 8 | 9 | ## Key Capabilities & Examples 10 | 11 | ### Noise Handling and Signal Quality 12 | ![50% reflectance endmembers with various levels of noise added](figures/figure_4.png) 13 | 14 | *Demonstration of how different SNR levels affect spectral signatures, from 10dB (high noise) to 60dB (low noise). This understanding is crucial for processing real sensor data.* 15 | 16 | ### Hyperspectral Data Visualization 17 | ![Decorrelation Stretched Image of Moffett Field, CA Dataset](figures/figure_13.png) 18 | 19 | *Decorrelation stretched image computed from bands 199, 126 and 51 (RGB) showing the Moffett Field, CA test site* 20 | 21 | ### Material Abundance Mapping 22 | ![Abundance map of Sagebrush](figures/figure_14.png) 23 | 24 | *Abundance map showing the distribution of Sagebrush across the Moffett Field, CA region* 25 | 26 | ![Abundance map of Lichen](figures/figure_15.png) 27 | 28 | *Abundance map showing the distribution of Lichen across the Moffett Field, CA region* 29 | 30 | ![Abundance map of Oak Tree](figures/figure_16.png) 31 | 32 | *Abundance map showing the distribution of Oak Trees across the Moffett Field, CA region* 33 | 34 | ![Abundance map of Antigorite](figures/figure_17.png) 35 | 36 | *Abundance map showing the distribution of Antigorite across the Moffett Field, CA region* 37 | *Progressive examples of material abundance mapping showing different material concentrations overlaid on Google Earth imagery of Moffett Field, CA. These maps demonstrate the toolbox's ability to identify and quantify material distributions across a scene.* 38 | 39 | ## Features 40 | 41 | The toolbox includes implementations of various hyperspectral exploitation algorithms: 42 | 43 | ### Core Capabilities 44 | - Target Detection Algorithms 45 | - RX Detector 46 | - Matched Filter 47 | - ACE (Adaptive Cosine Estimator) 48 | - Various hybrid detectors 49 | - Material Abundance Mapping 50 | - FCLS (Fully Constrained Least Squares) 51 | - UCLS (Unconstrained Least Squares) 52 | - NNLS (Non-Negative Least Squares) 53 | - Spectral Unmixing 54 | - VCA (Vertex Component Analysis) 55 | - ICA-EEA (Independent Component Analysis - Endmember Extraction Algorithm) 56 | - ATGP (Automated Target Generation Process) 57 | - Data Processing 58 | - MNF (Minimum Noise Fraction) 59 | - PCA (Principal Component Analysis) 60 | - Spectral Angle Mapper (SAM) 61 | - Spectral Information Divergence (SID) 62 | - Visualization Tools 63 | - 2D/3D data conversion 64 | - Colormap generation 65 | - Advanced plotting functions 66 | 67 | ### File Support 68 | - AVIRIS (.rfl) reading 69 | - ASD FieldSpec file reading 70 | - ENVI signature import 71 | - SPECPR file format support 72 | - Various hyperspectral data format conversions 73 | 74 | ## Dependencies 75 | 76 | - MATLAB (version requirements TBD) 77 | - [FastICA Toolbox](https://research.ics.aalto.fi/ica/fastica/) - Required for certain unmixing functions 78 | - Add to MATLAB's path using `addpath('path_to_fastica')` 79 | 80 | ## Installation 81 | 82 | 1. Clone the repository: 83 | ```bash 84 | git clone https://github.com/isaacgerg/matlabHyperspectralToolbox.git 85 | ``` 86 | 87 | 2. Add the toolbox to your MATLAB path: 88 | ```matlab 89 | addpath('path_to_toolbox') 90 | ``` 91 | 92 | 3. Install FastICA if needed for unmixing functionality 93 | 94 | ## Usage 95 | 96 | The toolbox includes several demo scripts to help you get started: 97 | - `hyperDemo.m` - General toolbox functionality 98 | - `hyperDemo_detectors.m` - Target detection algorithms 99 | - `hyperDemo_RIT_data.m` - Working with RIT dataset 100 | - `hyperDemo_ASD_reader.m` - Reading ASD FieldSpec data 101 | 102 | ## Function Documentation 103 | 104 | ### Target Detection Algorithms 105 | 106 | #### hyperAce.m (Adaptive Cosine/Coherence Estimator) 107 | Implements the ACE detector which normalizes the matched filter by both the background clutter energy and target energy: 108 | ``` 109 | ACE(x) = (s^T Σ^(-1) x)^2 / ((s^T Σ^(-1) s)(x^T Σ^(-1) x)) 110 | ``` 111 | where s is the target signature, x is the test pixel, and Σ is the background covariance matrix. 112 | 113 | #### hyperAmsd.m (Adaptive Matched Subspace Detector) 114 | Implements a GLRT detector for signals in subspace interference: 115 | ``` 116 | AMSD = (x^T P_B⊥ x - x^T P_(B,S)⊥ x) / (x^T P_(B,S)⊥ x) 117 | ``` 118 | where P_B⊥ is the projection onto the orthogonal complement of the background subspace. 119 | 120 | #### hyperAtgp.m (Automated Target Generation Process) 121 | Implements an orthogonal projection-based endmember extraction: 122 | 1. Finds pixel with largest magnitude 123 | 2. Projects data onto space orthogonal to found pixel 124 | 3. Repeats until desired number of endmembers found 125 | 126 | #### hyperGlrt.m (Generalized Likelihood Ratio Test) 127 | Implements the GLRT detector: 128 | ``` 129 | GLRT(x) = (s^T Σ^(-1) x)^2 / (s^T Σ^(-1) s) 130 | ``` 131 | Similar to ACE but without normalization by the pixel energy. 132 | 133 | ### Dimensionality Reduction & Whitening 134 | 135 | #### hyperMnf.m (Minimum Noise Fraction) 136 | Implements noise-adjusted principal components: 137 | 1. Estimates noise covariance Σn 138 | 2. Whitens data using noise covariance 139 | 3. Performs PCA on whitened data 140 | ``` 141 | MNF = eig(Σn^(-1/2) Σ Σn^(-1/2)) 142 | ``` 143 | 144 | #### hyperNapc.m (Noise-Adjusted Principal Components) 145 | Similar to MNF but uses a different noise estimation approach. 146 | 147 | #### hyperWhiten.m 148 | Implements data whitening: 149 | ``` 150 | x_white = Σ^(-1/2) x 151 | ``` 152 | where Σ^(-1/2) is computed via eigendecomposition. 153 | 154 | ### Spectral Unmixing 155 | 156 | #### hyperFcls.m (Fully Constrained Least Squares) 157 | Solves the constrained optimization problem: 158 | ``` 159 | min ||Ax - b||^2 subject to sum(x) = 1 and x ≥ 0 160 | ``` 161 | where A contains endmember signatures and x contains abundances. 162 | 163 | **Note**: This implementation contains a correction to the original formulation presented in "Fully Constrained Least-Squares Based Linear Unmixing" (Heinz, Chang, and Althouse, IEEE 1999). 164 | 165 | #### hyperNnls.m (Non-Negative Least Squares) 166 | Solves: 167 | ``` 168 | min ||Ax - b||^2 subject to x ≥ 0 169 | ``` 170 | 171 | #### hyperUcls.m (Unconstrained Least Squares) 172 | Solves the basic least squares problem: 173 | ``` 174 | min ||Ax - b||^2 175 | ``` 176 | 177 | #### hyperVca.m (Vertex Component Analysis) 178 | Implements VCA endmember extraction by: 179 | 1. Projecting data onto random vector 180 | 2. Finding extreme projections 181 | 3. Iterating with orthogonal projections 182 | 183 | ### Similarity Measures 184 | 185 | #### hyperCorr.m 186 | Computes Pearson correlation coefficient: 187 | ``` 188 | ρ = cov(x,y) / (σx σy) 189 | ``` 190 | 191 | #### hyperNormXCorr.m (Normalized Cross Correlation) 192 | Computes normalized cross correlation: 193 | ``` 194 | NCC = (x^T y) / (||x|| ||y||) 195 | ``` 196 | 197 | #### hyperSam.m (Spectral Angle Mapper) 198 | Computes spectral angle: 199 | ``` 200 | θ = arccos((x^T y) / (||x|| ||y||)) 201 | ``` 202 | 203 | #### hyperSid.m (Spectral Information Divergence) 204 | Computes information theoretic measure of spectral similarity using relative entropy. 205 | 206 | ### Statistical Detection 207 | 208 | #### hyperHud.m (Hybrid Unstructured Detector) 209 | Combines structured (matched filter) and unstructured (RX) detectors: 210 | ``` 211 | HUD = αRX + (1-α)MF 212 | ``` 213 | 214 | #### hyperRxDetector.m (Reed-Xiaoli Detector) 215 | Implements anomaly detection: 216 | ``` 217 | RX(x) = (x-μ)^T Σ^(-1) (x-μ) 218 | ``` 219 | where μ is the mean and Σ is the covariance. 220 | 221 | ### Preprocessing 222 | 223 | #### hyperConvexHullRemoval.m 224 | Removes pixels inside the convex hull of selected vertices. 225 | 226 | #### hyperDestreak.m 227 | Removes vertical striping artifacts in push-broom sensors. 228 | 229 | #### hyperNormalize.m 230 | Normalizes spectra to unit length: 231 | ``` 232 | x_norm = x / ||x|| 233 | ``` 234 | 235 | #### hyperResample.m 236 | Resamples spectra to new wavelength positions using interpolation. 237 | 238 | ### Independent Component Analysis 239 | 240 | #### hyperIcaEea.m (ICA Endmember Extraction) 241 | Uses FastICA to find statistically independent endmembers: 242 | 1. Reduces dimensionality via PCA 243 | 2. Applies ICA to find independent components 244 | 3. Projects back to original space 245 | 246 | #### hyperIcaComponentScores.m 247 | Computes abundance maps from ICA components. 248 | 249 | ### File I/O 250 | 251 | #### hyperReadAsd.m 252 | Reads ASD FieldSpec spectrometer files. 253 | 254 | #### hyperReadAvirisRfl.m 255 | Reads AVIRIS reflectance data. 256 | 257 | #### hyperReadSpecpr.m 258 | Reads SPECPR format spectral library files. 259 | 260 | #### hyperGetEnviSignature.m 261 | Reads spectral signatures from ENVI spectral libraries. 262 | 263 | ### Visualization 264 | 265 | #### hyperConvert2Colormap.m 266 | Converts hyperspectral image to RGB using dimensionality reduction. 267 | 268 | #### hyperConvert2d.m / hyperConvert3d.m 269 | Converts between 2D (samples × bands) and 3D (rows × cols × bands) formats. 270 | 271 | #### hyperImagesc.m / hyperImshow.m 272 | Enhanced visualization of hyperspectral data cubes. 273 | 274 | #### hyperMax2d.m 275 | Finds local maxima in 2D arrays. 276 | 277 | ### Performance Evaluation 278 | 279 | #### hyperPpi.m (Pixel Purity Index) 280 | Computes measure of pixel spectral purity through repeated projections. 281 | 282 | #### hyperRoc.m (Receiver Operating Characteristic) 283 | Computes ROC curves for detector performance evaluation. 284 | 285 | ## Related Projects 286 | 287 | Thanks to the permissive license of this toolbox, several derivative works have emerged: 288 | 289 | ### PySPTools 290 | Much of this codebase has been ported to Python in the [PySPTools project](https://pysptools.sourceforge.io/), providing these algorithms to the Python community. 291 | 292 | ### HyperSpectral Toolbox by David Kun 293 | [David Kun's fork](https://davidkun.github.io/HyperSpectralToolbox/) of this toolbox maintains the MATLAB implementation while adding several new features. 294 | 295 | ## Planned Features 296 | 297 | Future development priorities include: 298 | - Joint Affine Matched Filter 299 | - Enhanced Matched Filter with signature statistics 300 | - RAF-SAM (Improved Spectral Angle Mapper) 301 | - ELM (Empirical Line Method) for radiance-to-reflectance conversion 302 | - Advanced covariance matrix inversion methods 303 | - Quadratic Detector 304 | - Additional unmixing algorithms (SMACC, AMEE, NFINDR) 305 | - Antonia Plaza's FastPPI 306 | - Joshua Broaderwater's hybrid detectors 307 | 308 | ## Citation 309 | 310 | If you use this toolbox in your research, please cite: 311 | 312 | ```bibtex 313 | @Misc{matlab_hsi_toolbox, 314 | author = {Isaac Gerg}, 315 | title = {Open Source MATLAB Hyperspectral Toolbox}, 316 | howpublished = {\url{https://github.com/isaacgerg/matlabHyperspectralToolbox}}, 317 | year = {2006--2022} 318 | } 319 | ``` 320 | 321 | ## Contributing 322 | 323 | Derivative works must include the original citation. New contributors should add their names to the author line while maintaining the original author information. 324 | 325 | ## Contact 326 | 327 | Email: isaac.gerg@gergltd.com 328 | -------------------------------------------------------------------------------- /figures/figure_13.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/figures/figure_13.png -------------------------------------------------------------------------------- /figures/figure_14.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/figures/figure_14.png -------------------------------------------------------------------------------- /figures/figure_15.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/figures/figure_15.png -------------------------------------------------------------------------------- /figures/figure_16.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/figures/figure_16.png -------------------------------------------------------------------------------- /figures/figure_17.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/figures/figure_17.png -------------------------------------------------------------------------------- /figures/figure_4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/figures/figure_4.png -------------------------------------------------------------------------------- /ge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/ge.png -------------------------------------------------------------------------------- /hyperspectralToolbox/contents.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/contents.m -------------------------------------------------------------------------------- /hyperspectralToolbox/fnnls.m: -------------------------------------------------------------------------------- 1 | function [x,w] = fnnls(XtX,Xty,tol) 2 | %FNNLS Non-negative least-squares. 3 | % 4 | % Adapted from NNLS of Mathworks, Inc. 5 | % 6 | % x = fnnls(XtX,Xty) returns the vector X that solves x = pinv(XtX)*Xty 7 | % in a least squares sense, subject to x >= 0. 8 | % Differently stated it solves the problem min ||y - Xx|| if 9 | % XtX = X'*X and Xty = X'*y. 10 | % 11 | % A default tolerance of TOL = MAX(SIZE(XtX)) * NORM(XtX,1) * EPS 12 | % is used for deciding when elements of x are less than zero. 13 | % This can be overridden with x = fnnls(XtX,Xty,TOL). 14 | % 15 | % [x,w] = fnnls(XtX,Xty) also returns dual vector w where 16 | % w(i) < 0 where x(i) = 0 and w(i) = 0 where x(i) > 0. 17 | % 18 | % See also NNLS and FNNLSb 19 | 20 | % L. Shure 5-8-87 21 | % Revised, 12-15-88,8-31-89 LS. 22 | % (Partly) Copyright (c) 1984-94 by The MathWorks, Inc. 23 | 24 | % Modified by R. Bro 5-7-96 according to 25 | % Bro R., de Jong S., Journal of Chemometrics, 1997, 11, 393-401 26 | % Corresponds to the FNNLSa algorithm in the paper 27 | % 28 | % 29 | % Rasmus bro 30 | % Chemometrics Group, Food Technology 31 | % Dept. Dairy and Food Science 32 | % Royal Vet. & Agricultural 33 | % DK-1958 Frederiksberg C 34 | % Denmark 35 | % rb@kvl.dk 36 | % http://newton.foodsci.kvl.dk/rasmus.html 37 | 38 | 39 | % Reference: 40 | % Lawson and Hanson, "Solving Least Squares Problems", Prentice-Hall, 1974. 41 | 42 | % Copyright (c) 1999, Rasmus Bro 43 | % All rights reserved. 44 | % 45 | % Redistribution and use in source and binary forms, with or without 46 | % modification, are permitted provided that the following conditions are 47 | % met: 48 | % 49 | % * Redistributions of source code must retain the above copyright 50 | % notice, this list of conditions and the following disclaimer. 51 | % * Redistributions in binary form must reproduce the above copyright 52 | % notice, this list of conditions and the following disclaimer in 53 | % the documentation and/or other materials provided with the distribution 54 | % 55 | % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 56 | % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 57 | % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 58 | % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 59 | % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 60 | % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 61 | % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 62 | % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 63 | % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 64 | % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 65 | % POSSIBILITY OF SUCH DAMAGE. 66 | 67 | % initialize variables 68 | if nargin < 3 69 | tol = 10*eps*norm(XtX,1)*max(size(XtX)); 70 | end 71 | [m,n] = size(XtX); 72 | P = zeros(1,n); 73 | Z = 1:n; 74 | x = P'; 75 | ZZ=Z; 76 | w = Xty-XtX*x; 77 | 78 | % set up iteration criterion 79 | iter = 0; 80 | itmax = 30*n; 81 | 82 | % outer loop to put variables into set to hold positive coefficients 83 | while any(Z) & any(w(ZZ) > tol) 84 | [wt,t] = max(w(ZZ)); 85 | t = ZZ(t); 86 | P(1,t) = t; 87 | Z(t) = 0; 88 | PP = find(P); 89 | ZZ = find(Z); 90 | nzz = size(ZZ); 91 | z(PP')=(Xty(PP)'/XtX(PP,PP)'); 92 | z(ZZ) = zeros(nzz(2),nzz(1))'; 93 | z=z(:); 94 | % inner loop to remove elements from the positive set which no longer belong 95 | 96 | while any((z(PP) <= tol)) & iter < itmax 97 | 98 | iter = iter + 1; 99 | QQ = find((z <= tol) & P'); 100 | alpha = min(x(QQ)./(x(QQ) - z(QQ))); 101 | x = x + alpha*(z - x); 102 | ij = find(abs(x) < tol & P' ~= 0); 103 | Z(ij)=ij'; 104 | P(ij)=zeros(1,max(size(ij))); 105 | PP = find(P); 106 | ZZ = find(Z); 107 | nzz = size(ZZ); 108 | z(PP)=(Xty(PP)'/XtX(PP,PP)'); 109 | z(ZZ) = zeros(nzz(2),nzz(1)); 110 | z=z(:); 111 | end 112 | x = z; 113 | w = Xty-XtX*x; 114 | end 115 | 116 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperAce.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperAce(M, S) 2 | % HYPERACE Performs the adaptive cosin/coherent estimator algorithm 3 | % Performs the adaptive cosin/coherent estimator algorithm for target 4 | % detection. 5 | % 6 | % Usage 7 | % [results] = hyperAce(M, S) 8 | % Inputs 9 | % M - 2d matrix of HSI data (p x N) 10 | % S - 2d matrix of target endmembers (p x q) 11 | % Outputs 12 | % results - vector of detector output (N x 1) 13 | % 14 | % References 15 | % X Jin, S Paswater, H Cline. "A Comparative Study of Target Detection 16 | % Algorithms for Hyperspectral Imagery." SPIE Algorithms and Technologies 17 | % for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Vol 18 | % 7334. 2009. 19 | 20 | 21 | [p, N] = size(M); 22 | % Remove mean from data 23 | u = mean(M.').'; 24 | M = M - repmat(u, 1, N); 25 | S = S - repmat(u, 1, size(S,2)); 26 | 27 | R_hat = hyperCov(M); 28 | G = inv(R_hat); 29 | 30 | results = zeros(1, N); 31 | % From Broadwater's paper 32 | %tmp = G*S*inv(S.'*G*S)*S.'*G; 33 | tmp = (S.'*G*S); 34 | for k=1:N 35 | x = M(:,k); 36 | % From Broadwater's paper 37 | %results(k) = (x.'*tmp*x) / (x.'*G*x); 38 | results(k) = (S.'*G*x)^2 / (tmp*(x.'*G*x)); 39 | end 40 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperAmsd.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperAmsd(M, B, target) 2 | % HYPERAMSD Adaptive matched subspace detector (AMSD) algorithm 3 | % Performs the adaptive matched subspace detector (AMSD) algorithm for 4 | % target detection 5 | % 6 | % Usage 7 | % [results] = hyperAmsd(M, U, target) 8 | % Inputs 9 | % M - 2d matrix of HSI data (p x N) 10 | % B - 2d matrix of background endmebers (p x q) 11 | % target - target of interest (p x 1) 12 | % Outputs 13 | % results - vector of detector output (N x 1) 14 | % 15 | % References 16 | % Joshua Broadwater, Reuven Meth, Rama Chellappa. "A Hybrid Algorithms 17 | % for Subpixel Detection in Hyperspectral Imagery." IGARSS 004. Vol 3. 18 | % September 2004. 19 | 20 | [p, N] = size(M); 21 | I = eye(p); 22 | 23 | E = [B target]; 24 | P_B = I - (B * pinv(B)); 25 | P_Z = I - (E * pinv(E)); 26 | 27 | results = zeros(N, 1); 28 | tmp = P_B - P_Z; 29 | for k=1:N 30 | x = M(:,k); 31 | % Equation 16 32 | results(k) = (x.'*tmp*x) / (x.'*P_Z*x); 33 | end -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperAtgp.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/hyperAtgp.m -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperCem.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperCem(M, target) 2 | % HYPERCEM Performs constrained energy minimization (CEM) algorithm 3 | % Performs the constrained energy minimization algorithm for target 4 | % detection. 5 | % 6 | % Usage 7 | % [results] = hyperCem(M, target) 8 | % Inputs 9 | % M - 2d matrix of HSI data (p x N) 10 | % target - target of interest (p x 1) 11 | % Outputs 12 | % results - vector of detector output (N x 1) 13 | % 14 | % References 15 | % Qian Du, Hsuan Ren, and Chein-I Cheng. A Comparative Study of 16 | % Orthogonal Subspace Projection and Constrained Energy Minimization. 17 | % IEEE TGRS. Volume 41. Number 6. June 2003. 18 | 19 | [p, N] = size(M); 20 | % CEM uses the correlation matrix, NOT the covariance matrix. Therefore, 21 | % don't remove the mean from the data. 22 | R_hat = hyperCorr(M); 23 | Rinv = inv(R_hat); 24 | 25 | tmp = target'*Rinv*target; 26 | 27 | results = zeros(1, N); 28 | for k=1:N 29 | % Equation 6 30 | results(k) = (target'*Rinv*M(:,k)) / tmp; 31 | end -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperConvert2Colormap.m: -------------------------------------------------------------------------------- 1 | function [imgOut] = hyperConvert2Colormap(imgIn, cmap) 2 | %HYPERCONVERT2COLORMap Converts a matrix to a specified colormap 3 | % Converts a matrix into the specified colormap values. Useful 4 | % for writing float data to a color image (e.g. .png) file. 5 | % 6 | % Usage 7 | % [imgOut] = hyperConvert2Colormap(imgIn, cmap) 8 | % Inputs 9 | % imgIn - input matrix, must be 2D 10 | % cmap - (optional) Colormap to use. If not specified, jet is used. 11 | % Outputs 12 | % imgOut - 3D matrix containing corresponding jet colormap values 13 | 14 | if (ndims(imgIn) ~= 2) 15 | fprintf('Need a two dimensional image.'); 16 | return; 17 | end 18 | if (nargin == 1) 19 | tmpJet = jet; 20 | end 21 | tmpJet = cmap; 22 | s = size(tmpJet, 1); 23 | imgIn = hyperNormalize(imgIn); 24 | [h, w] = size(imgIn); 25 | imgOut = zeros(h, w, 3); 26 | for j=1:h 27 | for i=1:w 28 | v = tmpJet(round(imgIn(j, i)*(s-1))+1, :); 29 | imgOut(j, i, :) = v; 30 | end 31 | end -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperConvert2d.m: -------------------------------------------------------------------------------- 1 | function [M] = hyperConvert2d(M) 2 | % HYPERCONVERT2D Converts an HSI cube to a 2D matrix 3 | % Converts a 3D HSI cube (m x n x p) to a 2D matrix of points (p X N) 4 | % where N = mn 5 | % 6 | % Usage 7 | % [M] = hyperConvert2d(M) 8 | % Inputs 9 | % M - 3D HSI cube (m x n x p) 10 | % Outputs 11 | % M - 2D data matrix (p x N) 12 | 13 | if (ndims(M)>3 || ndims(M)<2) 14 | error('Input image must be m x n x p or m x n'); 15 | end 16 | if (ndims(M) == 2) 17 | numBands = 1; 18 | [h, w] = size(M); 19 | else 20 | [h, w, numBands] = size(M); 21 | end 22 | 23 | M = reshape(M, w*h, numBands).'; 24 | 25 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperConvert3d.m: -------------------------------------------------------------------------------- 1 | function [img] = hyperConvert3d(img, h, w, numBands) 2 | % HYPERCONVERT2D Converts an 2D matrix to a 3D data cube 3 | % Converts a 2D matrix (p x N) to a 3D data cube (m x n x p) 4 | % where N = m * n 5 | % 6 | % Usage 7 | % [M] = hyperConvert3d(M) 8 | % Inputs 9 | % M - 2D data matrix (p x N) 10 | % Outputs 11 | % M - 3D data cube (m x n x p) 12 | 13 | 14 | if (ndims(img) ~= 2) 15 | error('Input image must be p x N.'); 16 | end 17 | 18 | [numBands, N] = size(img); 19 | 20 | if (1 == N) 21 | img = reshape(img, h, w); 22 | else 23 | img = reshape(img.', h, w, numBands); 24 | end 25 | 26 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperConvexHullRemoval.m: -------------------------------------------------------------------------------- 1 | function normalizedU = hyperConvexHullRemoval(U,wavelengths) 2 | %HYPERCONVEXHULLREMOVAL Performs spectral normalization via convex hull removal 3 | % 4 | % Usage 5 | % [ normalizedU ] = hyperConvexHullRemoval( U, wavelengths ) 6 | % 7 | % Inputs 8 | % U - 2D HSI data (p x q) 9 | % wavelengths - Wavelength of each band (p x 1) 10 | % 11 | % Outputs 12 | % normalizedU - Data with convex hull removed (p x q) 13 | % 14 | % Author 15 | % Luca Innocenti 16 | % 17 | % References 18 | % Clark, R.N. and T.L. Roush (1984) Reflectance Spectroscopy: Quantitative 19 | % Analysis Techniques for Remote Sensing Applications, J. Geophys. Res., 89, 20 | % 6329-6340. 21 | 22 | % Metadata and formatting 23 | wavelengths = wavelengths(:); 24 | p = length(wavelengths); 25 | q = size(U,2); 26 | U = U.'; 27 | 28 | U(:,1) = 0; 29 | U(:,420) = 0; 30 | 31 | normalizedU = zeros(q,420); 32 | 33 | % The algorithm 34 | for s = 1:q, 35 | rifl = U(s,:); 36 | k = convhull(wavelengths,rifl'); 37 | c = [rifl(k); wavelengths(k)']; 38 | d = sortrows(c',2); 39 | 40 | xs = d(:,2); 41 | ys = d(:,1); 42 | [xsp, idx] = unique(xs); 43 | ysp = ys(idx); 44 | rifl_i = interp1(xsp,ysp,wavelengths'); 45 | 46 | for t = 1:420, 47 | if rifl_i(t) ~= 0 48 | normalizedU(s,t) = rifl(t)/rifl_i(t); 49 | else 50 | normalizedU(s,t) = 1; 51 | end 52 | end 53 | end 54 | 55 | normalizedU = normalizedU.'; 56 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperCorr.m: -------------------------------------------------------------------------------- 1 | function [R] = hyperCorr(M) 2 | % HYPERCORR Computes the sample autocorrelation matrix 3 | % hyperCorr compute the sample autocorrelation matrix of a 2D matrix. 4 | % 5 | % Usage 6 | % [R] = hyperCorr(M) 7 | % 8 | % Inputs 9 | % M - 2D matrix 10 | % Outputs 11 | % R - Sample autocorrelation matrix 12 | 13 | 14 | [p, N] = size(M); 15 | 16 | R = (M*M.')/N; 17 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperCov.m: -------------------------------------------------------------------------------- 1 | function [C] = hyperCov(M) 2 | % HYPERCOV Computes the covariance matrix 3 | % hyperCorr compute the sample covariance matrix of a 2D matrix. 4 | % 5 | % Usage 6 | % [C] = hyperCorr(M) 7 | % 8 | % Inputs 9 | % M - 2D matrix 10 | % Outputs 11 | % C - Sample covariance matrix 12 | 13 | [p, N] = size(M); 14 | % Remove mean from data 15 | u = mean(M.').'; 16 | for k=1:N 17 | M(:,k) = M(:,k) - u; 18 | end 19 | 20 | C = (M*M.')/(N-1); 21 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperDemo.m: -------------------------------------------------------------------------------- 1 | function hyperDemo 2 | % HYPERDEMO Demonstrates the hyperspectral toolbox 3 | clear; clc; dbstop if error; close all; 4 | %-------------------------------------------------------------------------- 5 | % Parameters 6 | resultsDir = 'results\\'; 7 | dataDir = 'data\\AVIRIS\\'; 8 | fastIcaDir = 'FastICA_25\\'; 9 | %-------------------------------------------------------------------------- 10 | 11 | fprintf('Storing results in %s directory.\n', resultsDir); 12 | mkdir(resultsDir); 13 | addpath(fastIcaDir); 14 | 15 | % Read in an HSI image and display one band 16 | slice = hyperReadAvirisRfl(sprintf('%s\\f970620t01p02_r03_sc02.a.rfl', dataDir), [1 100], [1 614], [132 132]); 17 | figure; imagesc(slice); axis image; colormap(gray); 18 | title('Band 132'); 19 | 20 | % Read part of AVIRIS data file that we will further process 21 | M = hyperReadAvirisRfl(sprintf('%s\\f970620t01p02_r03_sc02.a.rfl', dataDir), [1 100], [1 614], [1 224]); 22 | 23 | % Read AVIRIS .spc file 24 | lambdasNm = hyperReadAvirisSpc(sprintf('%s\\f970620t01p02_r03.a.spc', dataDir)); 25 | figure; plot(lambdasNm, 1:length(lambdasNm)); title('Band Number Vs Wavelengths'); grid on; 26 | xlabel('Wavelength [nm]'); ylabel('Band Number'); 27 | 28 | % NDVI - I believe this should ideally be done with radiance data and no 29 | % reflectance as we are doing here. 30 | ir = M(:,:,59); 31 | r = M(:,:,27); 32 | ndvi = (ir - r) ./ (ir + r); 33 | figure; imagesc(ndvi); title('NDVI of Image'); axis image; colorbar; 34 | 35 | % Isomorph 36 | [h, w, p] = size(M); 37 | M = hyperConvert2d(M); 38 | 39 | % Resample AVIRIS image. 40 | desiredLambdasNm = 400:(2400-400)/(224-1):2400; 41 | M = hyperResample(M, lambdasNm, desiredLambdasNm); 42 | 43 | % Remove low SNR bands. 44 | goodBands = [10:100 116:150 180:216]; 45 | M = M(goodBands, :); 46 | p = length(goodBands); 47 | 48 | % Demonstrate difference spectral similarity measurements 49 | M = hyperConvert3d(M, h, w, p); 50 | target = squeeze(M(32, 257, :)); 51 | figure; plot(desiredLambdasNm(goodBands), target); grid on; 52 | title('Target Signature; Pixel (32, 257)'); 53 | 54 | % Spectral Angle Mapper 55 | r = zeros(h, w); 56 | for i=1:h 57 | for j=1:w 58 | r(i, j) = abs(hyperSam(squeeze(M(i,j,:)), target)); 59 | end 60 | end 61 | figure; imagesc(r); title('Spectral Angle Mapper Result [radians]'); axis image; 62 | colorbar; 63 | 64 | % Spectral Information Divergence 65 | r = zeros(h, w); 66 | for i=1:h 67 | for j=1:w 68 | r(i, j) = abs(hyperSid(squeeze(M(i,j,:)), target)); 69 | end 70 | end 71 | figure; imagesc(r); title('Spectral Information Divergence Result'); axis image; 72 | colorbar; 73 | 74 | % Normalized Cross Correlation 75 | r = zeros(h, w); 76 | for i=1:h 77 | for j=1:w 78 | r(i, j) = abs((hyperNormXCorr(squeeze(M(i,j,:)), target))); 79 | end 80 | end 81 | figure; imagesc(r); title('Normalized Cross Correlation [0, 1]'); axis image; 82 | colorbar; 83 | 84 | % PPI 85 | U = hyperPpi(hyperConvert2d(M), 50, 1000); 86 | figure; plot(U); title('PPI Recovered Endmembers'); grid on; 87 | 88 | 89 | %-------------------------------------------------------------------------- 90 | % Perform a fully unsupervised exploitation chain using HFC, ATGP, and NNLS 91 | fprintf('Performing fully unsupervised exploitation using HFC, ATGP, and NNLS...'); 92 | M = hyperConvert2d(M); 93 | 94 | % Estimate number of endmembers in image. 95 | q = hyperHfcVd(M, [10^-3]); 96 | %q = 50; 97 | 98 | % PCA the data to remove noise 99 | %hyperWhiten(M) 100 | M = hyperPct(M, q); 101 | %p = q; 102 | 103 | % Unmix AVIRIS image. 104 | %U = hyperVca(M, q); 105 | U = hyperAtgp(M, q); 106 | figure; plot(U); title('ATGP Recovered Endmembers'); grid on; 107 | 108 | % Create abundance maps from unmixed endmembers. 109 | %abundanceMaps = hyperUcls(M, U); 110 | abundanceMaps = hyperNnls(M, U); 111 | %abundanceMaps = hyperFcls(M, U); 112 | % abundanceMaps = hyperNormXCorr(M, U); 113 | abundanceMaps = hyperConvert3d(abundanceMaps, h, w, q); 114 | 115 | for i=1:q 116 | tmp = hyperOrthorectify(abundanceMaps(:,:,i), 21399.6, 0.53418); 117 | figure; imagesc(tmp); colorbar; axis image; 118 | title(sprintf('Abundance Map %d', i)); 119 | hyperSaveFigure(gcf, sprintf('%s\\chain1 - mam - %d.png', resultsDir, i)); 120 | close(gcf); 121 | end 122 | fprintf('Done.\n'); 123 | %-------------------------------------------------------------------------- 124 | % Perform another fully unsupervised exploitation chain using ICA 125 | fprintf('Performing fully unsupervised exploitation using ICA...'); 126 | [U, abundanceMaps] = hyperIcaEea(M, q); 127 | abundanceMaps = hyperConvert3d(abundanceMaps, h, w, q); 128 | for i=1:q 129 | tmp = hyperOrthorectify(abundanceMaps(:,:,i), 21399.6, 0.53418); 130 | figure; imagesc(tmp); colorbar; axis image; 131 | title(sprintf('Abundance Map %d', i)); 132 | hyperSaveFigure(gcf, sprintf('%s\\chain2 - mam - %d.png', resultsDir, i)); 133 | close(gcf); 134 | end 135 | fprintf('Done.\n'); 136 | 137 | 138 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperDemo_ASD_reader.m: -------------------------------------------------------------------------------- 1 | clear; close all; clc; dbstop if error; 2 | 3 | %-------------------------------------------------------------------------- 4 | % This file demonstrates how to read data from an ASD Fieldspec 5 | % spectrometer. 6 | %-------------------------------------------------------------------------- 7 | % Parameters 8 | inputFilename1 = 'data\spectra\sample00000.asd'; 9 | inputFilename2 = 'data\spectra\gypsum.000'; 10 | %-------------------------------------------------------------------------- 11 | 12 | % Read from a file containing a reflectance signature 13 | [spectraReflectance, lambda] = hyperReadAsd(inputFilename2); 14 | % Display results 15 | figure; plot(lambda,spectraReflectance); grid on; 16 | title('Signature'); xlabel('Lambda [nm]'); ylabel('Reflectance [0,1]'); 17 | axis([350,2500,0,1]); 18 | 19 | % Read from a file containing digital number (DN) signature 20 | [measuredSpectra, lambda, referenceSpectra] = hyperReadAsd(inputFilename1); 21 | % Display results 22 | figure; plot(lambda,measuredSpectra); grid on; 23 | title('Measured Signature'); xlabel('Lambda [nm]'); 24 | ylabel('Digital Number'); 25 | figure; plot(lambda,referenceSpectra); grid on; 26 | title('Reference Signature'); xlabel('Lambda [nm]'); 27 | ylabel('Digital Number'); 28 | reflectance = measuredSpectra./referenceSpectra; 29 | figure; plot(lambda,reflectance); grid on; 30 | title('Dervied Reflectance'); xlabel('Lambda [nm]'); 31 | ylabel('Reflectance [0,1]'); 32 | axis([350,2500,0,1]); -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperDemo_RIT_data.m: -------------------------------------------------------------------------------- 1 | clear; close all; clc; dbstop if error; 2 | 3 | %-------------------------------------------------------------------------- 4 | % This demo process the data from the RIT Target Detection Blind Test 5 | % contest which is located at: http://dirsapps.cis.rit.edu/blindtest/ 6 | % To use this file, you select a target detection algorithm and a target to 7 | % find and then the script runs the algorithm and outputs the data into the 8 | % outputDir. Two files are outputted, a .img and a .hdr. You upload these 9 | % files to the RIT website and they are automatically scored. 10 | %-------------------------------------------------------------------------- 11 | % Parameters 12 | inputFilename = 'data\blind_test\HyMap\blind_test_refl.img'; 13 | fasticaToolboxPath = '..\matlab_hyperspectral_toolbox\trunk\FastICA_25'; 14 | targetFilenames = {'data\blind_test\SPL\F5\F5_f.txt'}; 15 | outputDir = 'RIT Data Results'; 16 | % See switch statement for algorithm choices 17 | algorithm = 'ace' 18 | %algorithm = 'rmf-sum'; 19 | %algorithm = 'plmf' 20 | %algorithm = 'matchedFilter'; 21 | %algorithm = 'sam' 22 | %-------------------------------------------------------------------------- 23 | 24 | addpath('gmm'); 25 | 26 | addpath(fasticaToolboxPath); 27 | mkdir(outputDir); 28 | 29 | % Read in the data 30 | w = 280; 31 | h = 800; 32 | p = 126; 33 | M = multibandread(inputFilename, [w h p], 'int16', 0, 'bil', 'ieee-le')/1e4; 34 | lData = hyperGetHymapWavelengthsNm(); 35 | 36 | % Read in target signatures 37 | [sig1, lSig] = hyperGetEnviSignature(targetFilenames{1}); 38 | 39 | % Get signature from data for comparison 40 | fsig1 = squeeze(M(122,495,:)); 41 | %sig1 = fsig1; 42 | 43 | % Resample data to commone wavelength set 44 | desiredLambdas = lData; 45 | sig1 = squeeze(hyperResample(sig1, lSig, desiredLambdas)); 46 | figure; plot(sig1); grid on; title('Signature 1'); 47 | xlabel('Wavelength [nm]'); ylabel('Reflectance [%]'); 48 | hold on; plot(fsig1, '--'); 49 | legend('Recorded', 'From Image'); 50 | 51 | goodBands = 1:p; %[3:63 69:93 98:123]; 52 | 53 | % Image sharpening 54 | if 0 55 | ff = fspecial('unsharp',0.2); 56 | for k=1:p 57 | M(:,:,k) = imfilter(M(:,:,k),ff,'same'); 58 | M(:,:,k) = imfilter(M(:,:,k),ff,'same'); 59 | %M(:,:,k) = imfilter(M(:,:,k),ff,'same'); 60 | end 61 | end 62 | 63 | figure; imagesc(M(:,:,40)); axis image; colormap(gray); 64 | 65 | % Try to discover in-situ to lab kernel. 66 | % TODO 67 | % sub(:,1) = M(144,515,:); 68 | % sub(:,2) = M(144,516,:); 69 | % sub(:,3) = M(144,517,:); 70 | % sub(:,4) = M(145,515,:); 71 | % sub(:,5) = M(145,516,:); 72 | % sub(:,6) = M(145,517,:); 73 | % sub(:,7) = M(146,515,:); 74 | % sub(:,8) = M(146,516,:); 75 | % sub(:,9) = M(146,517,:); 76 | % 77 | % alpha = pinv(sub)*sig1; %alpha = alpha ./ sum(alpha(:)); 78 | % err = sub*alpha - sig1; err = err - mean(err); badBands = find(abs(err)>0.02); 79 | % goodBands = setxor(1:p,badBands); 80 | % figure; plot(err); hold on; plot(sig1,'.'); plot(fsig1,'.-'); hold off; grid on; 81 | % legend({'err','lab sig','in situ sig'}) 82 | % alpha = reshape(alpha,3,3); 83 | % figure; imagesc(alpha); 84 | % 85 | % for k=1:p 86 | % %M(:,:,p) = conv2(M(:,:,p),alpha,'same'); 87 | % end 88 | 89 | % Emperical dervied 90 | goodBands = [3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 ... 91 | 19 20 22 23 24 26 28 29 31 32 33 34 35 36 37 ... 92 | 38 39 40 41 42 43 44 45 46 49 51 52 53 54 55 ... 93 | 56 57 58 59 60 61 62 66 69 70 71 72 86 87 88 ... 94 | 89 90 91 92 93 96 97 98 99 100 101 102 103 104 105 ... 95 | 106 107 108 109 110 111 112 113 115 116 117 119 120 121 122]; 96 | %sig1 = squeeze(hyperResample(sig1, lSig, desiredLambdas)); 97 | figure; plot(sig1(goodBands)); grid on; title('Signature 1 - good bands only'); 98 | xlabel('Wavelength [nm]'); ylabel('Reflectance [%]'); 99 | hold on; plot(fsig1(goodBands), '--'); 100 | legend('Recorded', 'From Image'); 101 | 102 | 103 | % Display data 104 | M = hyperConvert2d(M); 105 | %[M, H, snr] = hyperMnf(M, w, h); 106 | M_pct = hyperPct(M, 3); 107 | M_pct = hyperNormalize(hyperConvert3d(M_pct,w,h,3)); 108 | figure; imagesc(M_pct); axis image; title('Scene'); 109 | 110 | % Data conditioning 111 | M = M(goodBands, :); 112 | sig1 = sig1(goodBands); 113 | %fsig1 = fsig1(goodBands); 114 | %sig1 = fsig1; 115 | 116 | %q = hyperHfcVd(M); 117 | 118 | % Do PCT 119 | if 0 120 | M = [M sig1]; 121 | %[M,V] = hyperPct(M,size(M,1)); 122 | [M,V] = hyperPct(M,55); 123 | sig1 = M(:,end); 124 | M = M(:,1:end-1); 125 | p = size(M,1); 126 | goodBands = 1:p; 127 | end 128 | 129 | %q = hyperHfcVd(M); 130 | q = 39; 131 | 132 | algorithm = lower(algorithm); 133 | tic 134 | switch algorithm 135 | case 'ica-eea' 136 | [U, X] = hyperIcaEea(M, 50, sig1); 137 | r = X(1,:); 138 | r = hyperConvert3d(r, w, h, 1); 139 | case 'rx' 140 | r = hyperConvert3d(hyperRxDetector(M), w, h, 1); 141 | case 'matchedfilter' 142 | r = hyperConvert3d(hyperMatchedFilter(M, sig1), w, h, 1); 143 | case 'ace' 144 | r = hyperConvert3d(hyperAce(M, sig1), w, h, 1); 145 | case 'mace' 146 | r = hyperConvert3d(hyperMace(M, sig1), w, h, 1); 147 | case 'sid' 148 | r = hyperConvert3d(hyperSid(M, sig1), w, h, 1); 149 | case 'cem' 150 | r = hyperConvert3d(hyperCem(M, sig1), w, h, 1); 151 | case 'plmf' 152 | r = hyperPlmf(hyperConvert3d(M,w,h,p),sig1,9); 153 | case 'rmf-sum' 154 | r = hyperRmf(hyperConvert3d(M,w,h,p),sig1,11,'sum'); 155 | case 'rmf-meanlocal' 156 | r = hyperRmf(hyperConvert3d(M,w,h,p),sig1,11,'meanLocal'); 157 | case 'rmf-meangloballocal' 158 | r = hyperRmf(hyperConvert3d(M,w,h,p),sig1,11,'meanGlobalLocal'); 159 | case 'glrt' 160 | r = hyperConvert3d(hyperGlrt(M, sig1), w, h, 1); 161 | case 'osp' 162 | U = hyperAtgp(M, q, sig1); 163 | r = hyperConvert3d(hyperOsp(M, U, sig1), w, h, 1); 164 | case 'amsd' 165 | r = hyperConvert3d(hyperAmsd(M, U, sig1), w, h, 1); 166 | case 'hud' 167 | U = hyperAtgp(M, q, sig1); 168 | r = hyperConvert3d(hyperHud(M, U, sig1), w, h, 1); 169 | case 'nnls' 170 | U = hyperAtgp(M, q, sig1); 171 | r = hyperConvert3d(hyperNnls(M,U),w,h,q); 172 | r = r(:,:,1); 173 | case 'fcls' 174 | U = hyperAtgp(M, q, sig1); 175 | r = hyperConvert3d(hyperFcls(M,U),w,h,q); 176 | r = r(:,:,1); 177 | case 'ucls' 178 | U = hyperAtgp(M, q, sig1); 179 | r = hyperConvert3d(hyperUcls(M,U),w,h,q); 180 | r = r(:,:,1); 181 | case 'sam' 182 | r = (1./(eps+hyperConvert3d(hyperSam(M, sig1), w, h, 1))); 183 | otherwise 184 | error('Incorrect algorithm name specified!\n'); 185 | end 186 | toc 187 | 188 | % Display results and write to file 189 | figure; imagesc(r); axis image; colorbar; 190 | title(algorithm); 191 | 192 | [a,b]=sort(r(:),'descend'); 193 | tmp = a(1:20); 194 | figure; plot(tmp./tmp(1)); grid on; 195 | [x, y, val] = hyperMax2d(r); 196 | 197 | % d1 = r(122,494) 198 | % d2 = r(127,490) 199 | % N = prod(size(r)); 200 | % [v] = sort(r(:),'ascend'); 201 | % idx = find(v==d2); 202 | % N-idx 203 | % figure; hist(r(:),100); 204 | 205 | tmp = (hyperNormalize(r)*2^10); 206 | multibandwrite(tmp, sprintf('%s\\results.img', outputDir), 'bil', 'PRECISION', 'int16', 'MACHFMT', 'ieee-le'); 207 | 208 | [pd,fa] = hyperRoc(r); 209 | figure; plot(fa,pd,'.'); grid on; title(sprintf('%s\n%s',algorithm, targetFilenames{1})); 210 | 211 | 212 | 213 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperDemo_detectors.m: -------------------------------------------------------------------------------- 1 | function hyperDemo_detectors 2 | % HYPERDEMO_DETECTORS Demonstrates target detector algorithms 3 | clear; clc; dbstop if error; close all; 4 | %-------------------------------------------------------------------------- 5 | % Parameters 6 | resultsDir = 'results\\'; 7 | dataDir = 'data\\AVIRIS\\'; 8 | %-------------------------------------------------------------------------- 9 | 10 | mkdir(resultsDir); 11 | 12 | % Read part of AVIRIS data file that we will further process 13 | M = hyperReadAvirisRfl(sprintf('%s\\f970620t01p02_r03_sc02.a.rfl', dataDir), [1 100], [1 614], [1 224]); 14 | M = hyperNormalize(M); 15 | 16 | % Read AVIRIS .spc file 17 | lambdasNm = hyperReadAvirisSpc(sprintf('%s\\f970620t01p02_r03.a.spc', dataDir)); 18 | 19 | % Isomorph 20 | [h, w, p] = size(M); 21 | M = hyperConvert2d(M); 22 | 23 | % Resample AVIRIS image. 24 | desiredLambdasNm = 400:(2400-400)/(224-1):2400; 25 | M = hyperResample(M, lambdasNm, desiredLambdasNm); 26 | 27 | % Remove low SNR bands. 28 | goodBands = [10:100 116:150 180:216]; 29 | M = M(goodBands, :); 30 | p = length(goodBands); 31 | 32 | % Demonstrate difference spectral similarity measurements 33 | M = hyperConvert3d(M, h, w, p); 34 | target = squeeze(M(11, 77, :)); 35 | figure; plot(desiredLambdasNm(goodBands), target); grid on; 36 | title('Target Signature; Pixel (32, 257)'); 37 | 38 | M = hyperConvert2d(M); 39 | 40 | % RX Anomly Detector 41 | r = hyperRxDetector(M); 42 | r = hyperConvert3d(r.', h, w, 1); 43 | figure; imagesc(r); title('RX Detector Results'); axis image; 44 | colorbar; 45 | hyperSaveFigure(gcf, sprintf('%s\\rx detector.png', resultsDir)); 46 | 47 | % Constrained Energy Minimization (CEM) 48 | r = hyperCem(M, target); 49 | r = hyperConvert3d(r, h, w, 1); 50 | figure; imagesc(abs(r)); title('CEM Detector Results'); axis image; 51 | colorbar; 52 | hyperSaveFigure(gcf, sprintf('%s\\cem detector.png', resultsDir)); 53 | 54 | % Adaptive Cosine Estimator (ACE) 55 | r = hyperAce(M, target); 56 | r = hyperConvert3d(r, h, w, 1); 57 | figure; imagesc(r); title('ACE Detector Results'); axis image; 58 | colorbar; 59 | hyperSaveFigure(gcf, sprintf('%s\\ace detector.png', resultsDir)); 60 | 61 | % Signed Adaptive Cosine Estimator (S-ACE) 62 | r = hyperSignedAce(M, target); 63 | r = hyperConvert3d(r, h, w, 1); 64 | figure; imagesc(r); title('Signed ACE Detector Results'); axis image; 65 | colorbar; 66 | hyperSaveFigure(gcf, sprintf('%s\\signed ace detector.png', resultsDir)); 67 | 68 | % Matched Filter 69 | r = hyperMatchedFilter(M, target); 70 | r = hyperConvert3d(r, h, w, 1); 71 | figure; imagesc(r); title('MF Detector Results'); axis image; 72 | colorbar; 73 | hyperSaveFigure(gcf, sprintf('%s\\mf detector.png', resultsDir)); 74 | 75 | % Generalized Likehood Ratio Test (GLRT) detector 76 | r = hyperGlrt(M, target); 77 | r = hyperConvert3d(r, h, w, 1); 78 | figure; imagesc(r); title('GLRT Detector Results'); axis image; 79 | colorbar; 80 | hyperSaveFigure(gcf, sprintf('%s\\cem detector.png', resultsDir)); 81 | 82 | 83 | % Estimate background endmembers 84 | U = hyperAtgp(M, 5); 85 | 86 | % Hybrid Unstructured Detector (HUD) 87 | r = hyperHud(M, U, target); 88 | r = hyperConvert3d(r, h, w, 1); 89 | figure; imagesc(abs(r)); title('HUD Detector Results'); axis image; 90 | colorbar; 91 | hyperSaveFigure(gcf, sprintf('%s\\hud detector.png', resultsDir)); 92 | 93 | % Adaptive Matched Subspace Detector (AMSD) 94 | r = hyperAmsd(M, U, target); 95 | r = hyperConvert3d(r, h, w, 1); 96 | figure; imagesc(abs(r)); title('AMSD Detector Results'); axis image; 97 | colorbar; 98 | hyperSaveFigure(gcf, sprintf('%s\\amsd detector.png', resultsDir)); 99 | figure; mesh(r); title('AMSD Detector Results'); 100 | 101 | % Orthogonal Subspace Projection (OSP) 102 | r = hyperOsp(M, U, target); 103 | r = hyperConvert3d(r, h, w, 1); 104 | figure; imagesc(abs(r)); title('OSP Detector Results'); axis image; 105 | colorbar; 106 | hyperSaveFigure(gcf, sprintf('%s\\osp detector.png', resultsDir)); 107 | 108 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperDemo_mams_RIT_data.m: -------------------------------------------------------------------------------- 1 | clear; close all; clc; dbstop if error; 2 | 3 | %-------------------------------------------------------------------------- 4 | % This demo process the data from the RIT Target Detection Blind Test 5 | % contest which is located at: http://dirsapps.cis.rit.edu/blindtest/ 6 | %-------------------------------------------------------------------------- 7 | % Parameters 8 | inputFilename = 'data\self_test\HyMap\self_test_rad.img'; 9 | fasticaToolboxPath = '..\matlab_hyperspectral_toolbox\trunk\FastICA_25'; 10 | outputDir = 'RIT MAMS\'; 11 | %-------------------------------------------------------------------------- 12 | 13 | addpath(fasticaToolboxPath); 14 | mkdir(outputDir); 15 | 16 | % Read in the data 17 | h = 280; 18 | w = 800; 19 | p = 126; 20 | N = w*h; 21 | M = multibandread(inputFilename, [h w p], 'int16', 0, 'bil', 'ieee-le')/1e4; 22 | lData = hyperGetHymapWavelengthsNm(); 23 | 24 | % Select good bands. In this case, all bands are okay to use. 25 | goodBands = 1:p; 26 | 27 | % Display data 28 | M = hyperConvert2d(M); 29 | M_pct = hyperPct(M, 3); 30 | M_pct = hyperNormalize(hyperConvert3d(M_pct, h, w, 3)); 31 | figure; imagesc(M_pct); axis image; title('Scene'); 32 | 33 | % Data conditioning 34 | M = M(goodBands, :); 35 | 36 | % Compute the number of endmembers/materials in the scene. 37 | %q = hyperHfcVd(M); 38 | q = 53; 39 | 40 | modelErr = []; 41 | for q = 1:p 42 | % Find the endmembers/materials in the scene. 43 | fprintf('Searching for fundemental endmembers...\n'); 44 | [U,idx] = hyperAtgp(M, q); 45 | idx 46 | figure; plot(U); title('ATGP Recovered Endmembers'); grid on; 47 | 48 | % Create abundance maps from unmixed endmembers. 49 | fprintf('Generating material abundance maps (MAMs)...\n'); 50 | %abundanceMaps = hyperUcls(M, U); 51 | %abundanceMaps = hyperNnls(M, U); 52 | abundanceMaps = hyperFcls(M, U); 53 | % abundanceMaps = hyperNormXCorr(M, U); 54 | abundanceMaps = hyperConvert3d(abundanceMaps, h, w, q); 55 | 56 | % Display results and save figures to disk. 57 | for i=1:q 58 | figure; imagesc(abundanceMaps(:,:,i)); colorbar; axis image; 59 | title(sprintf('Abundance Map %d', i)); 60 | hyperSaveFigure(gcf, sprintf('%s\\chain1 - mam - %d.png', outputDir, i), 'wysiwyp'); 61 | close(gcf); 62 | end 63 | 64 | % Compute abundance fraction sums for each pixel. 65 | abundanceMaps = hyperConvert2d(abundanceMaps); 66 | tmpMap = zeros(h*w,1); 67 | for k=1:N 68 | tmpMap(k) = sum(abundanceMaps(:,k)); 69 | end 70 | tmpMap = hyperConvert3d(tmpMap, h, w, 1); 71 | figure; imagesc(tmpMap); colorbar; axis image; 72 | title('Sum of Each Pixel Abundance'); 73 | 74 | % Compute error between decomposed signature and real signature 75 | tmpMap = zeros(h*w,1); 76 | reconstructedM = U*abundanceMaps; 77 | for k=1:N 78 | tmpMap(k) = norm(reconstructedM(:,k)-M(:,k)); 79 | end 80 | tmpMap = hyperConvert3d(tmpMap, h, w, 1); 81 | figure; imagesc(tmpMap); colorbar; axis image; 82 | title('Model Error'); 83 | close all; 84 | 85 | modelErr(q) = sum(tmpMap(:)); 86 | end 87 | 88 | figure; plot(1:p,modelErr(1:p)); grid on; 89 | title('Model Error'); 90 | 91 | fprintf('Done.\n'); 92 | 93 | 94 | %--------------- 95 | t = tmpMap(:); 96 | [~,tMaxIdx]=max(t); 97 | figure;plot(M(:,tMaxIdx)); 98 | 99 | [a,b]=sort(t,'descend'); 100 | b(1:100) 101 | figure; plot(a); 102 | figure; hist(a,100); 103 | 104 | figure; plot(M(:,b(1:100))); 105 | title('100 worst model fits') 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperDestreak.m: -------------------------------------------------------------------------------- 1 | function [M, alpha, beta] = hyperDestreak(M) 2 | % HYPERDESTREAK Destreaks a hyperspectral data cube. 3 | % hyperDestreak removes vertical streaking artifacts from an HSI image. 4 | % 5 | % Usage 6 | % [M, alpha, beta] = hyperDestreak(M) 7 | % Inputs 8 | % M - 3D cube of HSI data. 9 | % Outputs 10 | % M - Destreaked data 11 | % alpha - mean value of column and band 12 | % beta - offset value of column and band 13 | % 14 | % References 15 | % Data, et al. "Processing E)-1 Hyperion Hypespectral Data to Support 16 | % the Application of Agricultural Index." IEEE TGRS. Vol 41. No 6. June 17 | % 2003. 18 | 19 | [h, w, p] = size(M); 20 | m = zeros(p,1); 21 | for k=1:p 22 | tmp = M(:,:,k); 23 | tmp = tmp(:); 24 | m(k) = mean(tmp); 25 | s(k) = std(tmp); 26 | for kk=1:w 27 | tmp = squeeze(M(:,kk,k)); 28 | ml = mean(tmp); 29 | sl = std(tmp); 30 | alpha(k,kk) = s(k) / sl; 31 | beta(k,kk) = m(k) - alpha(k,kk)*ml; 32 | tmp = alpha(k,kk)*tmp + beta(k,kk); 33 | M(:,kk,k) = tmp; 34 | end 35 | end 36 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperFcls.m: -------------------------------------------------------------------------------- 1 | function [ X ] = hyperFcls( M, U ) 2 | %HYPERFCLS Performs fully constrained least squares on pixels of M. 3 | % hyperFcls performs fully constrained least squares of each pixel in M 4 | % using the endmember signatures of U. Fully constrained least squares 5 | % is least squares with the abundance sum-to-one constraint (ASC) and the 6 | % abundance nonnegative constraint (ANC). 7 | % 8 | % Usage 9 | % [ X ] = hyperFcls( M, U ) 10 | % Inputs 11 | % M - HSI data matrix (p x N) 12 | % U - Matrix of endmembers (p x q) 13 | % Outputs 14 | % X - Abundance maps (q x N) 15 | % 16 | % References 17 | % "Fully Constrained Least-Squares Based Linear Unmixing." Daniel Heinz, 18 | % Chein-I Chang, and Mark L.G. Althouse. IEEE. 1999. 19 | 20 | if (ndims(U) ~= 2) 21 | error('M must be a p x q matrix.'); 22 | end 23 | 24 | [p1, N] = size(M); 25 | [p2, q] = size(U); 26 | if (p1 ~= p2) 27 | error('M and U must have the same number of spectral bands.'); 28 | end 29 | 30 | p = p1; 31 | X = zeros(q, N); 32 | Mbckp = U; 33 | for n1 = 1:N 34 | count = q; 35 | done = 0; 36 | ref = 1:q; 37 | r = M(:, n1); 38 | U = Mbckp; 39 | while not(done) 40 | als_hat = inv(U.'*U)*U.'*r; 41 | s = inv(U.'*U)*ones(count, 1); 42 | 43 | % IEEE Magazine method (http://www.planetary.brown.edu/pdfs/3096.pdf) 44 | % Contains correction to sign. Error in original paper. 45 | afcls_hat = als_hat - inv(U.'*U)*ones(count, 1)*inv(ones(1, count)*inv(U.'*U)*ones(count, 1))*(ones(1, count)*als_hat-1); 46 | 47 | % See if all components are positive. If so, then stop. 48 | if (sum(afcls_hat>0) == count) 49 | alpha = zeros(q, 1); 50 | alpha(ref) = afcls_hat; 51 | break; 52 | end 53 | % Multiply negative elements by their counterpart in the s vector. 54 | % Find largest abs(a_ij, s_ij) and remove entry from alpha. 55 | idx = find(afcls_hat<0); 56 | afcls_hat(idx) = afcls_hat(idx) ./ s(idx); 57 | [val, maxIdx] = max(abs(afcls_hat(idx))); 58 | maxIdx = idx(maxIdx); 59 | alpha(maxIdx) = 0; 60 | keep = setdiff(1:size(U, 2), maxIdx); 61 | U = U(:, keep); 62 | count = count - 1; 63 | ref = ref(keep); 64 | end 65 | X(:, n1) = alpha; 66 | end 67 | 68 | return; 69 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperFclsMatlab.m: -------------------------------------------------------------------------------- 1 | function [ X ] = hyperFclsMatlab( M, U ) 2 | %HYPERFCLSMATLAB Performs fully constrained least squares on pixels of M. 3 | % hyperFclsMatlab performs fully constrained least squares of each pixel 4 | % in M using the endmember signatures of U. Fully constrained least s 5 | % quares is least squares with the abundance sum-to-one constraint (ASC) 6 | % and the abundance nonnegative constraint (ANC). 7 | % This method utilizes Matlab's built-in solver to compute the answer. 8 | % 9 | % Usage 10 | % [ X ] = hyperFclsMatlab( M, U ) 11 | % Inputs 12 | % M - HSI data matrix (p x N) 13 | % U - Matrix of endmembers (p x q) 14 | % Outputs 15 | % X - Abundance maps (q x N) 16 | 17 | if (ndims(U) ~= 2) 18 | error('M must be a p x q matrix.'); 19 | end 20 | 21 | [p1, N] = size(M); 22 | [p2, q] = size(U); 23 | if (p1 ~= p2) 24 | error('M and U must have the same number of spectral bands.'); 25 | end 26 | 27 | Minv = pinv(U); 28 | X = zeros(q, N); 29 | for n1 = 1:N 30 | %X(:, n1) = Minv*M(:, n1); 31 | X(:, n1) = lsqlin(U, M(:, n1), [], [], ones(1,q), 1, zeros(q,1),[], []); 32 | end 33 | 34 | return; 35 | 36 | 37 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperFileFind.m: -------------------------------------------------------------------------------- 1 | function listOfMatchingFiles = hyperFileFind(startingDirectory, nameTemplate) 2 | % HYPERFILEFIND Searches through directories for files with specified name 3 | % Searches through the specified directory and sub-directories looking 4 | % for files matching the specified template. Returns the full, partial 5 | % path for each file matching the template 6 | % 7 | % Usage 8 | % [listOfMatchingFiles] = hyperFileFind(startingDirectory, nameTemplate) 9 | % Inputs 10 | % startingDirectory - Directory to begin search 11 | % nameTemplate - Template for file nameTemplate matching 12 | % Outputs 13 | % listOfMatchingFiles - Cell array with each element containing a string of a 14 | % file matching the name template. 15 | 16 | 17 | % Find all directories 18 | tmp = dir(startingDirectory); 19 | dTmp = []; 20 | for i=3:length(tmp) 21 | if (tmp(i).isdir == 1) 22 | dTmp = [dTmp; tmp(i)]; 23 | end 24 | end 25 | 26 | dTmp = [dTmp; dir(fullfile(startingDirectory, nameTemplate))]; 27 | listOfMatchingFiles = {}; 28 | for i=1:length(dTmp) 29 | if (dTmp(i).isdir == 1) 30 | listOfMatchingFiles = [listOfMatchingFiles; hyperFileFind(fullfile(startingDirectory, dTmp(i).name), nameTemplate)]; 31 | else 32 | listOfMatchingFiles = [listOfMatchingFiles; fullfile(startingDirectory, dTmp(i).name)]; 33 | end 34 | end 35 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperGetEnviSignature.m: -------------------------------------------------------------------------------- 1 | function [refl, lambdaNm] = hyperGetEnviSignature(filename) 2 | % HYPERGETENVISIGNATURE Reads an ENVI hyperspectral reflectance signature 3 | % hyperGetEnviSignature reads the RIT Target Detection Blind Test 4 | % signature files. 5 | % 6 | % Usage 7 | % [refl, lambdaNm] = hyperGetEnviSignature(filename) 8 | % 9 | % Input 10 | % filename - Filename of signature. 11 | % Output 12 | % refl - Reflectance values [0, 1]. 13 | % lambdaNm - corresponding wavelengths in nanometers 14 | 15 | fid = fopen(filename); 16 | 17 | for k=1:3 18 | dummy = fgetl(fid); 19 | end 20 | 21 | num = 1; 22 | while 1 23 | tmp = fgetl(fid); 24 | if (tmp == -1), break, end; 25 | v = sscanf(tmp, '%f'); 26 | refl(num) = v(2); 27 | lambdaNm(num) = v(1); 28 | num = num + 1; 29 | end 30 | 31 | refl = refl / 100; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperGetHymapWavelengthsNm.m: -------------------------------------------------------------------------------- 1 | function lambdaNm = hyperGetHymapWavelengthsNm() 2 | % HYPERGETHYMAPWAVELENGTHSNM Returns list of wavelengths for Hymap Sensor 3 | % 4 | % Usage 5 | % lambdaNm = hyperGetHymapWavelengthsNm() 6 | % 7 | % Inputs 8 | % None 9 | % Outputs 10 | % lambdaNm - Hymap instrument wavelengths in nanometers 11 | 12 | lambdaNm = [... 13 | 453.799988, 467.399994, 481.899994, 496.899994, 511.700012, 526.500000, ... 14 | 541.599976, 556.500000, 571.200012, 585.900024, 600.700012, 615.500000,... 15 | 630.000000, 644.299988, 658.900024, 673.599976, 688.000000, 702.400024,... 16 | 716.900024, 731.299988, 745.400024, 759.599976, 773.900024, 788.099976,... 17 | 802.200012, 816.299988, 830.700012, 844.900024, 858.900024, 872.500000,... 18 | 874.799988, 891.900024, 907.299988, 922.799988, 938.599976, 954.099976,... 19 | 969.200012, 984.400024, 999.900024, 1014.900024, 1029.900024, 1045.099976,... 20 | 1060.099976, 1074.599976, 1089.199951, 1104.099976, 1118.599976, 1133.000000,... 21 | 1147.400024, 1161.800049, 1176.000000, 1190.199951, 1204.300049, 1218.300049,... 22 | 1232.099976, 1246.099976, 1260.199951, 1274.099976, 1287.599976, 1301.199951,... 23 | 1315.199951, 1328.900024, 1389.300049, 1404.199951, 1419.300049, 1433.500000,... 24 | 1448.000000, 1462.400024, 1477.000000, 1490.900024, 1504.800049, 1518.599976,... 25 | 1532.500000, 1546.099976, 1559.800049, 1573.199951, 1586.400024, 1599.500000,... 26 | 1612.800049, 1626.000000, 1638.800049, 1651.699951, 1664.500000, 1677.199951,... 27 | 1689.699951, 1702.300049, 1714.900024, 1727.300049, 1739.500000, 1751.800049,... 28 | 1764.000000, 1776.000000, 1788.000000, 1799.900024, 1952.400024, 1971.699951,... 29 | 1991.000000, 2010.099976, 2029.000000, 2048.000000, 2067.199951, 2086.199951,... 30 | 2104.800049, 2123.000000, 2141.000000, 2159.100098, 2177.000000, 2194.600098,... 31 | 2213.399902, 2231.000000, 2248.199951, 2265.699951, 2283.100098, 2300.600098,... 32 | 2317.600098, 2334.500000, 2351.100098, 2367.600098, 2384.399902, 2401.100098,... 33 | 2417.699951, 2433.699951, 2449.600098, 2465.300049, 2480.899902, 2496.300049]; 34 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperGlrt.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperGlrt(M, t) 2 | % HYPERGLRT Performs the generalized liklihood test ratio algorithm 3 | % Performs the generalized liklihood test ratio algorithm for target 4 | % detection. 5 | % 6 | % Usage 7 | % [results] = hyperGlrt(M, U, target) 8 | % Inputs 9 | % M - 2d matrix of HSI data (p x N) 10 | % t - target of interest (p x 1) 11 | % Outputs 12 | % results - vector of detector output (N x 1) 13 | % 14 | % References 15 | % T F AyouB, "Modified GLRT Signal Detection Algorithm," IEEE 16 | % Transactions on Aerospace and Electronic Systems, Vol 36, No 3, July 17 | % 2000. 18 | 19 | [p, N] = size(M); 20 | 21 | % Remove mean from data 22 | u = mean(M.').'; 23 | M = M - repmat(u, 1, N); 24 | t = t - u; 25 | 26 | R = inv(hyperCov(M)); 27 | 28 | results = zeros(1, N); 29 | for k=1:N 30 | x = M(:,k); 31 | results(k) = ((t'*R*x)^2) / ((t'*R*t)*(1 + x'*R*x)); 32 | end -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperHfcVd.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/hyperHfcVd.m -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperHud.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperHud(M, B, S) 2 | % HYPERHUD Performs the hybrid unstructured detector (HUD) algorithm 3 | % Performs the hybrid unstructured detector algorithm for target 4 | % detection. 5 | % 6 | % Usage 7 | % [results] = hyperHud(M, B, S) 8 | % Inputs 9 | % M - 2d matrix of HSI data (p x N) 10 | % B - 2d matrix of background endmembers (p x q) 11 | % S - 2d matrix of target endmembers (p x #target_sigs) 12 | % Outputs 13 | % results - vector of detector output (N x 1) 14 | % 15 | % References 16 | % J Broadwater & R Chellappa. "Hybrid Detectors for Subpixel Targets." 17 | % IEEE PAMI. Vol 29. No 11. November 2007. 18 | 19 | 20 | [p, N] = size(M); 21 | % Remove mean from data 22 | u = mean(M.').'; 23 | M = M - repmat(u, 1, N); 24 | S = S - repmat(u, 1, size(S,2)); 25 | 26 | numTargets = size(S,2); 27 | %sigma = 1e-5; 28 | E = [S B]; 29 | %E = [sigma*E; ones(1,size(E,2))]; 30 | q = size(E, 2); 31 | 32 | R_hat = (M*M.')/N; 33 | G = inv(R_hat); 34 | 35 | results = zeros(1, N); 36 | 37 | R = ones(q,1); 38 | P = R - 1; 39 | % TODO - put in the whitened version of fcls 40 | a_hat_tmp = hyperNnls(M, E); 41 | %a_hat_tmp = hyperFcls(M, E); 42 | for k=1:N 43 | x = M(:,k); 44 | a_hat = a_hat_tmp(:,k); 45 | % Take the top r values from a_hat where r is number of targets. We 46 | % are only interested in the abundances for the targets. From J 47 | % Broadwater email 11/17/09. 48 | a_hat = a_hat(1:numTargets); 49 | % % x = [sigma*x; 1]; 50 | % % FCLS optimzation 51 | % lambda = zeros(q,1); 52 | % aPrev = lambda; 53 | % for kk=1:100 54 | % a_hat = inv(E.'*G*E)*E.'*G*x - inv(E.'*G*E)*lambda; 55 | % norm(a_hat-aPrev) 56 | % lambda = E.'*G*(x-E*a_hat); 57 | % idx = find(a_hat>0); 58 | % P(idx) = 1; 59 | % R(idx) = 0; 60 | % aPrev = a_hat; 61 | % end 62 | % a_hat = a_hat(1:numTargets); 63 | results(k) = (x.'*G*S*a_hat) / (x.'*G*x); 64 | end -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperIcaComponentScores.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/hyperIcaComponentScores.m -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperIcaEea.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/hyperIcaEea.m -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperImagesc.m: -------------------------------------------------------------------------------- 1 | function [rgb] = hyperImagesc(img, bands) 2 | %UNTITLED1 Summary of this function goes here 3 | % Usage: plotAvirisRgb(img, bands) 4 | 5 | [h, w, p] = size(img); 6 | 7 | if (nargin == 1) 8 | bands = [1 round(p/2) p]; 9 | end 10 | blue = img(:,:,bands(1)); 11 | green = img(:,:,bands(2)); 12 | red = img(:,:,bands(3)); 13 | 14 | rgb = zeros(size(img, 1), size(img, 2), 3); 15 | rgb(:,:,1) = hyperNormalize(red); 16 | rgb(:,:,2) = hyperNormalize(green); 17 | rgb(:,:,3) = hyperNormalize(blue); 18 | 19 | rgb = decorrstretch(rgb); 20 | red = rgb(:,:,1); 21 | green = rgb(:,:,2); 22 | blue = rgb(:,:,3); 23 | rgb(:,:,1) = adapthisteq(red); 24 | rgb(:,:,2) = adapthisteq(green); 25 | rgb(:,:,3) = adapthisteq(blue); 26 | 27 | imshow(rgb); axis image; 28 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperImshow.m: -------------------------------------------------------------------------------- 1 | function [rgb] = hyperImshow( img, bands ) 2 | %UNTITLED1 Summary of this function goes here 3 | % Detailed explanation goes here 4 | 5 | 6 | [h, w, p] = size(img); 7 | 8 | if (nargin == 1) 9 | bands = [p round(p/2) 1]; 10 | end 11 | red = img(:,:,bands(1)); 12 | green = img(:,:,bands(2)); 13 | blue = img(:,:,bands(3)); 14 | 15 | rgb = zeros(size(img, 1), size(img, 2), 3); 16 | rgb(:,:,1) = adapthisteq(red); 17 | rgb(:,:,2) = adapthisteq(green); 18 | rgb(:,:,3) = adapthisteq(blue); 19 | 20 | imshow(rgb); axis image; 21 | 22 | 23 | 24 | % tmp = zeros(100, 614, 3); 25 | % tmp(:,:,1) = histeq((img(:,:, [36]))); 26 | % tmp(:,:,2) = histeq((img(:,:, [24]))); 27 | % tmp(:,:,3) = histeq((img(:,:, [12]))); 28 | % image(tmp); -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperMatchedFilter.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperMatchedFilter(M, t) 2 | % TODO Fix this 3 | % HYPERACE Performs the adaptive cosin/coherent estimator algorithm 4 | % Performs the adaptive cosin/coherent estimator algorithm for target 5 | % detection. 6 | % 7 | % Usage 8 | % [results] = hyperAce(M, S) 9 | % Inputs 10 | % M - 2d matrix of HSI data (p x N) 11 | % S - 2d matrix of target endmembers (p x q) 12 | % Outputs 13 | % results - vector of detector output (N x 1) 14 | % 15 | % References 16 | % X Jin, S Paswater, H Cline. "A Comparative Study of Target Detection 17 | % Algorithms for Hyperspectral Imagery." SPIE Algorithms and Technologies 18 | % for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Vol 19 | % 7334. 2009. 20 | 21 | 22 | [p, N] = size(M); 23 | % Remove mean from data 24 | u = mean(M.').'; 25 | M = M - repmat(u, 1, N); 26 | t = t - u; 27 | 28 | R_hat = hyperCov(M); 29 | G = inv(R_hat); 30 | 31 | results = zeros(1, N); 32 | tmp = t.'*G*t; 33 | for k=1:N 34 | x = M(:,k); 35 | results(k) = (x.'*G*t)/tmp; 36 | end 37 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperMax2d.m: -------------------------------------------------------------------------------- 1 | function [x, y, val] = hyperMax2d(mat) 2 | % HYPERMAX2D Finds the max value and position in a matrix 3 | % 4 | % Usage 5 | % [x, y, val] = hyperMax2d(mat) 6 | % Inputs 7 | % mat - Input matrix 8 | % Outputs 9 | % x - X position of maximum value 10 | % y - Y position of maximum value 11 | % val - Maximum value in matrix 12 | 13 | [dum, y] = max(mat); 14 | [val, y] = max(dum); 15 | [dum, x] = max(mat(:,y)); -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperMnf.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/hyperMnf.m -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperNapc.m: -------------------------------------------------------------------------------- 1 | function [M, H, noiseFractions] = hyperNacp(M, h, w) 2 | % HYPERMNF Performs the noise adjusted principal component transform (NACP) 3 | % hyperMnf performs the noise adjust principal component transform on the 4 | % data and uses spatial (row) offsets of the data to estimate the 5 | % covariance matrix of the data. 6 | % 7 | % Usage 8 | % M = hyperNacp(M, h, w) 9 | % Inputs 10 | % M - 2D matrix (p x N) 11 | % h - height of image in pixels 12 | % w - width of image in pixels 13 | % Outputs 14 | % M - 2D transformed data 15 | % H - 2D transformation matrix 16 | % noiseFractions - Estimates of the noise fraction for each band 17 | % 18 | % References 19 | % C-I Change and Q Du, "Interference and Noise-Adjusted Principal 20 | % Components Analysis," IEEE TGRS, Vol 36, No 5, September 1999. 21 | 22 | [p, N] = size(M); 23 | 24 | % Remove mean from data 25 | u = mean(M.').'; 26 | for k=1:N 27 | M(:,k) = M(:,k) - u; 28 | end 29 | 30 | % Compute to rotation of the signal+noise 31 | sigmaZ = hyperCov(M); 32 | M = hyperConvert3d(M, h, w, p); 33 | 34 | % Estimate the covariance of the noise. 35 | dX = zeros(h-1, w, p); 36 | for i=1:(h-1) 37 | dX(i, :, :) = M(i, :, :) - M(i+1, :, :); 38 | end 39 | dX = hyperConvert2d(dX); 40 | 41 | % Compute the covariance of the noise signal estimate. 42 | sigmaN = hyperCov(dX); 43 | 44 | % Orthonormalize the noise subspace. 45 | [U,deltaN,E] = svd(sigmaN); 46 | F = E*inv(sqrt(deltaN)); % Rotation components of noise orthonormalized 47 | % F now whitens the noise. 48 | 49 | % Rotates the signal+noise cov so that the noise is whitened (all noise 50 | % powers are equal) 51 | sigmaAdj = F'*sigmaZ*F; 52 | 53 | [U,gammaAdj,G] = svd(sigmaAdj); 54 | H = G*F; 55 | 56 | % Compute noise fractions 57 | noiseFractions = diag(gammaAdj); 58 | 59 | % Perform transform 60 | M = H*hyperConvert2d(M); 61 | 62 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperNnls.m: -------------------------------------------------------------------------------- 1 | function [ X ] = hyperNnls( M, U ) 2 | %HYPERNNLS Performs non-negative constrained least squares on pixels of M. 3 | % hyperFcls performs non-negative constrained least squares of each pixel 4 | % in M using the endmember signatures of U. Non-negative constrained least 5 | % squares with the abundance nonnegative constraint (ANC). 6 | % Utilizes the method of Bro. 7 | % 8 | % Usage 9 | % [ X ] = hyperNnls( M, U ) 10 | % Inputs 11 | % M - HSI data matrix (p x N) 12 | % U - Matrix of endmembers (p x q) 13 | % Outputs 14 | % X - Abundance maps (q x N) 15 | % 16 | % References 17 | % Bro R., de Jong S., Journal of Chemometrics, 1997, 11, 393-401 18 | 19 | if (ndims(M) ~= 2) 20 | error('M must be a p x N matrix.'); 21 | end 22 | if (ndims(U) ~= 2) 23 | error('M must be a p x q matrix.'); 24 | end 25 | 26 | [p1, N] = size(M); 27 | [p2, q] = size(U); 28 | if (p1 ~= p2) 29 | error('M and U must have the same number of spectral bands.'); 30 | end 31 | 32 | Minv = pinv(U); 33 | X = zeros(q, N); 34 | MtM = U.'*U; 35 | for n1 = 1:N 36 | drawnow; 37 | %X(:, n1) = Minv*M(:, n1); 38 | %X(:, n1) = lsqlin(U, M(:, n1), [], [], ones(1,q), 1, zeros(q,1),[], []); 39 | X(:, n1) = fnnls(MtM, U.' * M(:,n1)); 40 | %X(:, n1) = lsqnonneg(U, M(:, n1)); 41 | end 42 | 43 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperNormXCorr.m: -------------------------------------------------------------------------------- 1 | function [ result ] = hyperNormXCorr( a, b ) 2 | % HYPERNORMXCORR Computes the normalized cross correlation 3 | % hyperNormXCorr computes the normalized cross correlation between two 4 | % vectors. The value returned is in [-1. 1] 5 | % 6 | % Usage 7 | % [ result ] = hyperNormXCorr( a, b ) 8 | % Inputs 9 | % a - Vector 1. 10 | % b - Vector 2. 11 | % Outputs 12 | % result - Normalized cross-correlation result. 13 | 14 | if (size(a, 2) ~= 1) 15 | N = size(a, 2); 16 | q = size(b, 2); 17 | result = zeros(q, N); 18 | for x=1:N 19 | for y=1:q 20 | result(y, x) = abs(hyperNormXCorr(a(:, x), b(:, y))); 21 | end 22 | end 23 | else 24 | a = a(:); b = b(:); 25 | s = length(a); 26 | %err = normxcorr2(a, b); 27 | err = sum((a-mean(a)).*(b-mean(b))) / (std(a)*std(b)); 28 | err = err * (1/(s-1)); 29 | result = err; 30 | end 31 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperNormalize.m: -------------------------------------------------------------------------------- 1 | function [ normalizedM ] = hyperNormalize( M ) 2 | %HYPERNORMALIZE Normalized data to be in range [0, 1] 3 | % hyperNormalize Normalizes data to be in range [0, 1] 4 | % 5 | % Usage 6 | % hyperNormalize(M) 7 | % Inputs 8 | % M - Input data 9 | % Outputs 10 | % normalizedM - Normalized data 11 | 12 | minVal = min(M(:)); 13 | maxVal = max(M(:)); 14 | 15 | normalizedM = M - minVal; 16 | if (maxVal == minVal) 17 | normalizeData = zeros(size(M)); 18 | else 19 | normalizedM = normalizedM ./ (maxVal-minVal); 20 | end 21 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperOrthorectify.m: -------------------------------------------------------------------------------- 1 | function [ imgOut ] = hyperOrthorectify( imgIn, altitude, hpbw ) 2 | %HYPERORTHORECTIFY Orthorectifies areal observed data. 3 | % Orthorectifies areal observed data using nearest neighbor interpolation. 4 | % 5 | % Inputs 6 | % imgIn Input image (m x n) or (m x n x p) 7 | % altitude Sensor altitude (meters) 8 | % hpbw Half power beam width (radians). 9 | % Outputs 10 | % imgOut Orthorectified image. 11 | 12 | % Input parameters 13 | if (ndims(imgIn) == 2) 14 | [h, w] = size(imgIn); 15 | p = 1; 16 | elseif (ndims(imgIn) == 3) 17 | [h, w, p] = size(imgIn); 18 | end 19 | 20 | radPerPix = hpbw/w; 21 | x = tan(hpbw/2)*altitude; % m 22 | gsd = altitude*radPerPix; % m 23 | n = x/gsd; 24 | 25 | outImg = zeros(h, floor(n)*2, p); 26 | for k=1:p 27 | for j=1:h 28 | for i=-floor(n):1:floor(n)-1 29 | boresiteDistance = gsd*i; 30 | theta = atan(boresiteDistance/altitude); 31 | imagePix = round(theta / radPerPix); 32 | imgOut(j, floor(n)+i+1, k) = imgIn(j, (w/2)+imagePix+1, k); 33 | end 34 | end 35 | end 36 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperOsp.m: -------------------------------------------------------------------------------- 1 | function [nu] = hyperOsp(M, U, target) 2 | % HYPEROSP Performs the othogonal subspace projection (OSP) algorithm 3 | % Performs the othogonal subspace projection algorithm for target 4 | % detection. 5 | % 6 | % Usage 7 | % [results] = hyperOsp(M, U, target) 8 | % Inputs 9 | % M - 2d matrix of HSI data (p x N) 10 | % U - 2d matrix of background endmebers (p x q) 11 | % target - target of interest (p x 1) 12 | % Outputs 13 | % results - vector of detector output (N x 1) 14 | % 15 | % References 16 | % Qian Du, Hsuan Ren, and Chein-I Cheng. "A Comparative Study of 17 | % Orthogonal Subspace Projection and Constrained Energy Minimization." 18 | % IEEE TGRS. Volume 41. Number 6. June 2003. 19 | 20 | [p, N] = size(M); 21 | 22 | % Equation 3 23 | P_U = eye(p) - U * pinv(U); 24 | 25 | % For abundance estimation 26 | % Equation 4 27 | %w_osp = inv(target.'*P_U*target) * P_U * target; 28 | 29 | tmp = target'*P_U*target; 30 | nu = zeros(N, 1); 31 | for k=1:N 32 | nu(k) = (target'*P_U*M(:,k))/tmp; 33 | end -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperPct.m: -------------------------------------------------------------------------------- 1 | function [M_pct, V, lambda] = hyperPct(M, q) 2 | %HYPERPCA Performs the principal components transform (PCT) 3 | % hyperPct performs the principal components transform on a data matrix. 4 | % 5 | % Usage 6 | % [M_pct, V] = hyperPct(M, q) 7 | % Inputs 8 | % M - 2D matrix (p x N) 9 | % q - number of components to keep 10 | % Outputs 11 | % M_pct - 2D matrix (q x N) which is result of transform 12 | % V - Transformation matrix. 13 | % lambda - eigenvalues 14 | % 15 | % References 16 | % http://en.wikipedia.org/wiki/Principal_component_analysis 17 | 18 | [p, N] = size(M); 19 | 20 | % Remove the data mean 21 | u = mean(M.').'; 22 | %M = M - repmat(u, 1, N); 23 | M = M - (u*ones(1,N)); 24 | 25 | % Compute covariance matrix 26 | C = (M*M.')/N; 27 | 28 | % Find eigenvalues of covariance matrix 29 | [V, D] = eigs(C, q); 30 | 31 | % Transform data 32 | M_pct = V'*M; 33 | 34 | lambda = diag(D); 35 | 36 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperPlmf.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperPlmf(M, t, windowSize) 2 | % HYPERPLMF Performs the PCA local matched filter (PLMF) target detection algorithm 3 | % Performs the PCA local matched filter (PLMF) target detection algorithm. 4 | % 5 | % Usage 6 | % [results] = hyperPlmf(M, target, windodwSize) 7 | % Inputs 8 | % M - dd matrix of HSI data (m x n x p) 9 | % t - target of interest (p x 1) 10 | % Outputs 11 | % results - vector of detector output (m x n) 12 | % 13 | % References 14 | % Sofa, Geva, Rotman. "Improved covariance matrices for point target detection in hyperspectral 15 | % data." IEEE International Conference on Microwaves, Communications, Antennas and Electronics 16 | % Systems, 2009. COMCAS 2009. 17 | 18 | % windowSize must be odd number 19 | if ~mod(windowSize,2) 20 | error('windowSize must be an odd number.') 21 | end 22 | 23 | if (length(size(M)) ~= 3) 24 | error('M must be 3-dimensional matrix.') 25 | end 26 | 27 | [h,w,p] = size(M); 28 | N = h*w; 29 | 30 | % Remove mean from the target 31 | M = hyperConvert2d(M); 32 | u = mean(M.').'; 33 | 34 | [Mpca,V,lambda] = hyperPct(M,p); 35 | t_pct = V.'*(t-u); 36 | 37 | % Create map to get neighbors 38 | map = 1:N; 39 | map = reshape(map,h,w); 40 | 41 | R_hat = hyperCov(Mpca); 42 | G = inv(R_hat); 43 | 44 | results = zeros(h,w); 45 | s = floor(windowSize/2)+1; 46 | for k=s:(h-s) 47 | for kk=s:(w-s) 48 | midIdx = map(k,kk); 49 | neighborhoodIdx = map((k-s+1):(k+s-1),(kk-s+1):(kk+s-1)); 50 | 51 | Mlocal = M(:,neighborhoodIdx(:)); 52 | [~,~,lambdaLocal] = hyperPct(Mlocal,p); 53 | 54 | y = Mpca(:,midIdx); 55 | results(k,kk) = sum((t_pct.*y)./max(lambdaLocal,lambda)); 56 | end 57 | end 58 | 59 | 60 | 61 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperPpi.m: -------------------------------------------------------------------------------- 1 | function [U] = hyperPpi(M, q, numSkewers) 2 | % HYPERPPI Performs the pixel purity index (PPI) algorithm 3 | % Performs the pixel purity index algorithm for endmember finding. 4 | % 5 | % Usage 6 | % [U] = hyperPpi(M, q, numSkewers) 7 | % Inputs 8 | % M - 2d matrix of HSI data (p x N) 9 | % q - Number of endmembers to find 10 | % numSkewers - Number of "skewer" vectors to project data onto. 11 | % Outputs 12 | % U - Recovered endmembers (p x N) 13 | 14 | [p, N] = size(M); 15 | 16 | % Remove data mean 17 | u = mean(M.').'; 18 | M = M - repmat(u, 1, N); 19 | 20 | % Generate skewers 21 | skewers = randn(p, numSkewers); 22 | 23 | votes = zeros(N, 1); 24 | for kk=1:numSkewers 25 | % Project all the data onto a skewer 26 | tmp = abs(skewers(:,kk).'*M); 27 | [val, idx] = max(tmp); 28 | votes(idx) = votes(idx) + 1; 29 | end 30 | 31 | [val, idx] = sort(votes, 'descend'); 32 | U = M(:, idx(1:q)); 33 | 34 | 35 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperReadAsd.m: -------------------------------------------------------------------------------- 1 | function [measuredSpectrum, lambda, referenceSpectrum] = hyperReadAsd(filename) 2 | % HYPERREADASD Reads spectra from an ASD Fieldspec spectrometer (e.g. .asd) 3 | % Reads in the measured and reference spectra from an ASD Fieldspec 4 | % spectrometer. If two output arguments are specified, hyperReadAsd assumes 5 | % the file contains reflectance. If three output arguments are specified, 6 | % hyperReadAsd assumes the file contains measured and reference spectra 7 | % in units of digital number (DN). 8 | % 9 | % Usage 10 | % [measuredSpectrum, lambda] = hyperReadAsd(filename) 11 | % [measuredSpectrum, lambda, referenceSpectrum] = hyperReadAsd(filename) 12 | % Inputs 13 | % filename - input filename (.asd) 14 | % Outputs 15 | % measuredSpectrum - measured spectrum of material (2151 x 1) 16 | % lambda - wavelenghts of spectra (2151 x 1) 17 | % referenceSpectrum (optional) - reference spectrum ("white" spectrum) 18 | % 19 | % Notes 20 | % Reflectance can be obtained by: 21 | % reflectance = measuredSpectrum./referenceSpectrum; 22 | % 23 | % Author 24 | % Luca Innocenti 25 | % 26 | % References 27 | % None 28 | 29 | % Open the file 30 | fid = fopen(filename, 'r'); 31 | 32 | if (2 == nargout) 33 | fseek(fid,484,'bof'); 34 | measuredSpectrum = zeros(2151,1); 35 | lambda = zeros(2151,1); 36 | for i=1:2151, 37 | lambda(i) = 349 + i; 38 | measuredSpectrum(i) = fread(fid,1,'single'); 39 | end 40 | fclose(fid); 41 | return; 42 | end 43 | 44 | lungh_nota = 0; 45 | fseek(fid, 0, 'bof'); 46 | % Get factory name 47 | nome_ditta = char(fread(fid, 3, 'uint8')); %Factory Name 48 | % Get note 49 | note = char(fread(fid,157,'uint8')); 50 | 51 | % count the note length string 52 | tt = isstrprop(note,'alphanum'); 53 | for f=1:157, 54 | if tt(f) == 1 55 | lungh_nota = f; 56 | f = 157; 57 | end 58 | end 59 | 60 | %Extract Metadata from header file 61 | %Not needed for spectrum 62 | 63 | %Time of acquisition 64 | fseek(fid, 160, 'bof'); 65 | sec_acq = fread(fid,1,'uint8'); %seconds 66 | fseek(fid, 162, 'bof'); 67 | minsec_acq = fread(fid,1,'uint8'); %minutes 68 | fseek(fid, 164, 'bof'); 69 | ora_acq = fread(fid,1,'uint8'); %hours 70 | fseek(fid, 166, 'bof'); 71 | giorno_acq = fread(fid,1,'uint8'); %day 72 | fseek(fid, 168, 'bof'); 73 | mese_acq = fread(fid,1,'uint8'); %month 74 | fseek(fid, 170, 'bof'); 75 | anno_acq = fread(fid,1,'uint8'); %years from 1900 76 | fseek(fid, 172, 'bof'); 77 | wday_acq = fread(fid,1,'uint8'); 78 | fseek(fid, 174, 'bof'); 79 | wdayy_acq = fread(fid,1,'uint16'); 80 | fseek(fid, 178, 'bof'); 81 | ver_programma = fread(fid,1,'uint8'); %software version 82 | fseek(fid, 179, 'bof'); 83 | ver_file = fread(fid,1,'uint8'); %file version 84 | 85 | %Data acquisition metadata 86 | fseek(fid, 180, 'bof'); 87 | itime = fread(fid,1,'uint8'); 88 | fseek(fid, 181, 'bof'); 89 | dc_corr = fread(fid,1,'uint8'); 90 | fseek(fid, 182, 'bof'); 91 | dc_time = fread(fid,1,'uint32'); 92 | data_type = fread(fid,1,'uint8'); 93 | ref_time = fread(fid,1,'uint32'); 94 | ch1_wavel = fread(fid,1,'uint8'); 95 | wavel_step = fread(fid,1,'uint8'); 96 | data_format = fread(fid,1,'uint8'); 97 | old_dc_count = fread(fid,1,'uint8'); 98 | old_ref_count = fread(fid,1,'uint8'); 99 | old_sample_count = fread(fid,1,'uint8'); 100 | application = fread(fid,1,'uint8'); 101 | channels = fread(fid,1,'uint8'); 102 | fseek(fid, 425, 'bof'); 103 | dc_count = fread(fid,1,'uint16'); 104 | white_count = fread(fid,1,'uint16'); 105 | fseek(fid, 431, 'bof'); 106 | instrument_type = fread(fid,1,'uint8'); 107 | fseek(fid, 390, 'bof'); 108 | integration_time = fread(fid,1,'uint16'); 109 | fo = fread(fid,1,'uint16'); 110 | dc_correction_value = fread(fid,1,'uint16'); 111 | fseek(fid, 398, 'bof'); 112 | calibration = fread(fid,1,'uint16'); 113 | 114 | %Spectrum 115 | referenceSpectrum = zeros(2151,1); 116 | measuredSpectrum = zeros(2151,1); 117 | lambda = zeros(2151,1); 118 | 119 | for x=1:2151, 120 | lambda(x) = 349 + x; 121 | end 122 | 123 | fseek(fid, 484, 'bof'); 124 | for i = 1:2151, 125 | measuredSpectrum(i) = fread(fid,1,'double'); 126 | 127 | end 128 | 129 | fseek(fid, 17712+lungh_nota, 'bof'); 130 | for i = 1:2151, 131 | referenceSpectrum(i) = fread(fid,1,'double'); 132 | end 133 | 134 | % Test Fixture 135 | % plot(lambda,referenceSpectrum) 136 | % title ('Digital Number White Reference'); 137 | % xlabel('Lambda (nm)'); 138 | % ylabel('Digital Number'); 139 | % axis([350,2500,0,max(referenceSpectrum)]) 140 | % 141 | % 142 | % figure 143 | % plot(lambda,measuredSpectrum) 144 | % title ('Digital Number Sample'); 145 | % xlabel('Lambda (nm)'); 146 | % ylabel('Digital Number'); 147 | % 148 | % axis([350,2500,0,max(measuredSpectrum)]) 149 | % 150 | % reflectance = measuredSpectrum./referenceSpectrum; 151 | % 152 | % figure 153 | % plot(lambda, reflectance) 154 | % title ('Reflectance'); 155 | % xlabel('Lambda (nm)'); 156 | % ylabel('Reflectance'); 157 | % 158 | % axis([350,2500,0,1]); 159 | 160 | fclose(fid); 161 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperReadAvirisRfl.m: -------------------------------------------------------------------------------- 1 | function [ M, wavelengths_nm ] = hyperReadAvirisRfl(filename, height, width, bands ) 2 | %HYPERREADAVIRISRFL Reads AVIRIS generated reflectance and .spc files. 3 | % This function reads AVIRIS .rfl, refelctance data, files. Optionally, it 4 | % reads in the corresponding .spc file to obtain the the wavelengths observed 5 | % by the sensor. 6 | % 7 | % Usage 8 | % [M, wavelengths_nm] = hyperReadAvirisRfl(filename, height, width, bands) 9 | % Intput 10 | % filename - filename image filename 11 | % height - vector of height range 12 | % width - vector of width range 13 | % bands - vector of band range 14 | % Output 15 | % M - reflectance data 16 | % wavelengths_nm - Wavelengths of reflectance data in nm. 17 | % 18 | % Format of the .rfl is below. Taken from the AVIRIS readme file. 19 | % 20 | % *.rfl AVIRIS INVERTED REFLECTANCE IMAGE DATA 21 | % 22 | % Contents: AVIRIS inverted reflectance data multipled by 10000 and stored as 23 | % 16-bit integers. 24 | % File type: BINARY 16-bit signed integer IEEE. 25 | % Units: 10000 times reflectance factor 26 | % Format: Band interleaved by pixel (channel, sample, line) with dimensions 27 | % (224, 614, 512). The last scene may be less than 512. To 28 | % calculate the number of lines divide the file size by 275,072 29 | % bytes per line. 30 | % 31 | % Example: 32 | % [img, lambda]= readAviris('f970620t01p02_r03_sc02.a.rfl', [1 100], [1 614], [1 224]); 33 | % Reads in all bands and rows of reflectance data from scanlines 1 to 100. 34 | % 35 | % Copyright (C) 2007 Isaac Gerg. All rights reserved. 36 | 37 | % Extract root filename. 38 | [shortFilename, pth] = findLast(filename, '\'); 39 | if (pth > 1) 40 | filePath = filename(1:pth); 41 | else 42 | filePath = ''; 43 | end 44 | [tmp, pos] = findLast(shortFilename, '.'); 45 | if (pos > 1) 46 | rootFilename = shortFilename(1:pos-1); 47 | else 48 | rootFilename = shortFilename; 49 | end 50 | [tmp, pth] = findLast(rootFilename, '_'); 51 | rootFilename = rootFilename(1:pth-1); 52 | 53 | % Parse .spc file 54 | if (nargout==2) 55 | spcFilename = sprintf('%s%s%s', filePath, rootFilename, '.a.spc'); 56 | wavelengths_nm = hyperReadAvirisSpc(spcFilename); 57 | end 58 | 59 | % Read in the reflectance data. 60 | fid = fopen(filename, 'r', 'ieee-be'); 61 | data_type = 'int16'; 62 | interleave = 'bip'; 63 | M = multibandread(filename, [512 614 224], data_type, 0, interleave, 'ieee-be',... 64 | {'Row', 'Range', [height]}, {'Column', 'Range', [width]}, ... 65 | {'Band', 'Range', [bands]} ); 66 | 67 | % Normalize to proper reflectance units. 68 | M = M ./ 10e3; 69 | 70 | return; 71 | 72 | 73 | %------------------------------------------------------------------------------- 74 | function [answer, pos] = findLast(str, char) 75 | slashes = find(str == char); 76 | if (length(slashes) > 0) 77 | lastSlash = slashes(end); 78 | else 79 | lastSlash = 0; 80 | end 81 | pos = lastSlash; 82 | answer = str(lastSlash+1:end); 83 | return; 84 | %------------------------------------------------------------------------------- 85 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperReadAvirisSpc.m: -------------------------------------------------------------------------------- 1 | function [lambda] = hyperReadAvirisSpc(filename) 2 | %HYPERREADAVIRISSPC Reads AVIRIS .spc files. 3 | % hyperReadAvirisSpc reads AVIRIS files containing information about the 4 | % wavelengths sampled during a collect with the AVIRIS sensor. 5 | % 6 | % Usage 7 | % [lambda] = hyperReadAvirisSpc(filename) 8 | % Input 9 | % filename - input filename of .spc file. 10 | % Output 11 | % lambda - wavelengths contained in .spc file. 12 | 13 | 14 | fid = fopen(filename, 'r'); 15 | 16 | i = 1; 17 | done = false; 18 | while not(done) 19 | txt = fgetl(fid); 20 | if (txt == -1) 21 | break; 22 | end 23 | a = sscanf(txt, '%g %g %g %g %g'); 24 | lambda(i) = a(1); 25 | i = i+1; 26 | end 27 | return; 28 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperReadSpecpr.m: -------------------------------------------------------------------------------- 1 | function [ records, spectra, rawSpectra ] = hyperReadSpecpr( filename ) 2 | %HYPERREADSPECPR Reads USGS Specpr files. 3 | % hyperReadSpecpr reads USGS Specpr files. 4 | % 5 | % Usage 6 | % [ records, spectra, rawSpectra ] = hyperReadSpecpr( filename ) 7 | % Inputs 8 | % filename - Input filename 9 | % Outputs 10 | % records - Individual records 11 | % spectra - The spectra post-processed 12 | % rawSpectra - The raw spectra 13 | % 14 | % References 15 | % http://speclab.cr.usgs.gov/specpr-format.html. 16 | dbstop if error; 17 | f = fopen(filename, 'r'); 18 | if (f == -1) 19 | error(sprintf('Failed to open file: %s'), filename); 20 | end 21 | 22 | % Ignore the first record. 23 | r = uint32(fread(f, 1536, 'uint8')); 24 | 25 | firstTime = 1; 26 | records = {}; 27 | spectra = []; 28 | rawSpectra = {}; 29 | numRawSpectra = 0; 30 | numRecords = 0; 31 | 32 | done = 0; 33 | while (not(done)) 34 | r = uint32(fread(f, 1536/4, 'uint32', 'ieee-be')); 35 | if (length(r) == 0) 36 | break; 37 | end 38 | numRecords 39 | r = swapbytes(r); 40 | r = typecast(r, 'uint8'); 41 | % First two bits of file. I am making this verbose here so it is clear what I 42 | % am doing. 43 | firstTwoBits = dec2bin(bitand(r(4), 3)); 44 | 45 | % Parse first two bits. 46 | if (firstTwoBits == '10') 47 | % This is a text record. Skip. 48 | numRecords = numRecords + 1; 49 | elseif (firstTwoBits == '0') 50 | % This is an actual (initial) data record. 51 | if (not(firstTime)) 52 | numRecords = numRecords+1; 53 | records{record.irecno} = record; 54 | end 55 | record = []; 56 | data = []; 57 | firstTime = 0; 58 | iband = int32(zeros(2, 1)); 59 | record.ititl = char(r(5:44)).'; 60 | record.usernm = char(r(45:52)).'; 61 | iscta = typecast(r(53:56), 'int32'); 62 | isctb = typecast(r(57:60), 'int32'); 63 | jdatea = typecast(r(61:64), 'int32'); 64 | jdateb = typecast(r(65:68), 'int32'); 65 | istb = typecast(r(69:72), 'int32'); 66 | isra = typecast(r(73:76), 'int32'); 67 | isdec = typecast(r(77:80), 'int32'); 68 | record.itchan = swapbytes(typecast(r(81:84), 'int32')); 69 | irmas = typecast(r(85:88), 'int32'); 70 | revs = typecast(r(89:92), 'int32'); 71 | iband(1) = typecast(r(93:96), 'int32'); 72 | iband(2) = typecast(r(97:100), 'int32'); 73 | record.irwav = swapbytes(typecast(r(101:104), 'int32')); 74 | record.irespt = swapbytes(typecast(r(105:108), 'int32')); 75 | record.irecno = swapbytes(typecast(r(109:112), 'int32')); 76 | itpntr = typecast(r(113:116), 'int32'); 77 | ihist = char(r(117:176)).'; 78 | mhist = char(r(177:472)).'; 79 | nruns = typecast(r(473:476), 'int32'); 80 | siangl = typecast(r(477:480), 'int32'); 81 | seangl = typecast(r(481:484), 'int32'); 82 | sphase = typecast(r(485:488), 'int32'); 83 | iwtrns = typecast(r(489:492), 'int32'); 84 | itimch = typecast(r(493:496), 'int32'); 85 | xnrm = typecast(r(497:500), 'int32'); 86 | scatim = typecast(r(501:504), 'int32'); 87 | timint = typecast(r(505:508), 'int32'); 88 | tempd = typecast(r(509:512), 'int32'); 89 | data = swapbytes(typecast(r(513:1536), 'single')); 90 | % Remove null data samples. Set to zero instead of -1.23e34. 91 | data(find(data < -1e34)) = 0; 92 | record.data = data; 93 | elseif (firstTwoBits == '1') 94 | % Continuation of data values. 95 | cData = swapbytes(typecast(r(5:1536), 'single')); 96 | cData(find(cData < -1e34)) = 0; 97 | data = [data; cData]; 98 | record.data = data; 99 | else 100 | numRecords = numRecords + 1; 101 | end 102 | end 103 | 104 | % Convert to an array of signatures. 105 | % Resample to model AVIRIS sensor 106 | high = 2.40; 107 | low = 0.4; 108 | numBands = 224; 109 | %d.data = sortrows(d.data, 1); 110 | %[q, w, r ]= unique(d.data(:,1)); 111 | %d.data = d.data(w, :); 112 | %lambda = d.data(:, 1); 113 | %reflectance = d.data(:, 2); 114 | s = length(records); 115 | numSpectra = 0; 116 | for q=1:s 117 | if (isempty(records{q})) 118 | continue; 119 | end 120 | if (records{q}.irwav == 0) 121 | continue; 122 | end 123 | 124 | if (not(isempty(strfind(records{q}.ititl, 'error')))) 125 | continue; 126 | end 127 | if (not(isempty(strfind(records{q}.ititl, 'Error')))) 128 | continue; 129 | end 130 | if (not(isempty(strfind(records{q}.ititl, 'Bandpass')))) 131 | continue; 132 | end 133 | if (not(isempty(strfind(records{q}.ititl, 'Wavelengths')))) 134 | continue; 135 | end 136 | % Find wavelengths 137 | if (isempty(records{records{q}.irwav})) 138 | continue; 139 | end 140 | lambdas = records{records{q}.irwav}.data; 141 | spectrum = records{q}.data; 142 | if (length(lambdas) ~= length(spectrum)) 143 | fprintf('Error %d !!!\n', q); 144 | continue; 145 | end 146 | numRawSpectra = numRawSpectra + 1; 147 | rawSpectra{numRawSpectra}.name = records{q}.ititl; 148 | rawSpectra{numRawSpectra}.wavelengths = lambdas; 149 | rawSpectra{numRawSpectra}.reflectance = spectrum; 150 | goodIdx = find(lambdas > 0); 151 | lambdas = lambdas(goodIdx); 152 | spectrum = spectrum(goodIdx); 153 | % Ensure we have proper lower and upper bounds. 154 | if (lambdas(1) > low) 155 | %fprintf('Bad file: Lower wavelength value missing.'); 156 | lambdas = [low; lambdas]; 157 | spectrum = [spectrum(1); spectrum]; 158 | end 159 | if (lambdas(end) < high) 160 | %fprintf('Bad file: Upper wavelength value missing.\n'); 161 | %d.data = []; 162 | %fclose(fid); 163 | %return; 164 | lambdas = [lambdas; high]; 165 | spectrum = [spectrum; spectrum(end)]; 166 | end 167 | % Resample 168 | records{q}.ititl 169 | ts = timeseries(spectrum, lambdas); 170 | inc = (high-low) / (numBands-1); 171 | c = resample(ts, [low:inc:high], 'zoh'); 172 | numSpectra = numSpectra + 1; 173 | spectra(numSpectra).data = [c.time c.data [1:numBands].']; 174 | spectra(numSpectra).name = records{q}.ititl; 175 | end 176 | 177 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperResample.m: -------------------------------------------------------------------------------- 1 | function [ M_resampled ] = hyperResample( M, currentWaveLengths, desiredWaveLengths ) 2 | %HYPERRESAMPLE Resamples hyperspectral data to specified wavelenghts 3 | % hyperResample resamples hyperspectral data with specified wavelengths 4 | % to a new set of wavelengths. 5 | % 6 | % Usage 7 | % [ output ] = hyperResample( M, currentWaveLengths, desiredWaveLengths ) 8 | % Inputs 9 | % M - HSI data (p x N) 10 | % currentWavelengths - Wavelengths of M. (p x 1) 11 | % desiredWavelengths - Desired wavelengths of M. 12 | % Output 13 | % M_resampled - Resampled version of M 14 | 15 | 16 | numDim = ndims(M); 17 | 18 | if (numDim == 3) 19 | h = size(M, 1); 20 | w = size(M, 2); 21 | numBands = size(M, 3); 22 | %M = reshape(M, w*h, numBands).'; 23 | M = hyperConvert2d(M); 24 | elseif (numDim == 2) 25 | w = size(M, 2); 26 | numBands = size(M, 1); 27 | end 28 | 29 | % Determine if desiredWaveLengths is a subrage of currentWaveLengths 30 | if (min(desiredWaveLengths) < min(currentWaveLengths)) 31 | sprintf('Desired wavelenths outside of lower range.\n'); 32 | return; 33 | end 34 | if (max(desiredWaveLengths) > max(currentWaveLengths)) 35 | sprintf('Desired wavelenths outside of upper range.\n'); 36 | return; 37 | end 38 | 39 | % Resample to desired bands. 40 | ts = timeseries(M, currentWaveLengths); 41 | ts = resample(ts, desiredWaveLengths, 'linear'); 42 | M_resampled = ts.data; 43 | clear tmp; 44 | clear M; 45 | 46 | if (numDim == 3) 47 | %output = reshape(output, h, w, length(desiredWaveLengths)); 48 | M_resampled = hyperConvert3d(M, w, h, length(desiredWaveLengths)); 49 | end 50 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperRmf.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperRmf(M, t, windowSize, algorithm) 2 | % HYPERPLMF Performs the regularlzed matched filter (RMF) target detection algorithm 3 | % Performs the regularized matched filter (PLMF) target detection algorithm. 4 | % 5 | % Usage 6 | % [results] = hyperRmf(M, target, windodwSize) 7 | % Inputs 8 | % M - dd matrix of HSI data (m x n x p) 9 | % t - target of interest (p x 1) 10 | % windowSize - window size designating local pixel region (scalar) 11 | % algorithm - 'sum' designates sum of local and global eigenvalues. 12 | % 'meanLocal' desiginates to use the mean of the local 13 | % eigenvalues. 14 | % 'meanGlobalLocal' designates to use the mean of the local 15 | % and global eigenvalues. 16 | % Outputs 17 | % results - vector of detector output (m x n) 18 | % 19 | % References 20 | % Sofa, Geva, Rotman. "Improved covariance matrices for point target detection in hyperspectral 21 | % data." IEEE International Conference on Microwaves, Communications, Antennas and Electronics 22 | % Systems, 2009. COMCAS 2009. 23 | 24 | % windowSize must be odd number 25 | if ~mod(windowSize,2) 26 | error('windowSize must be an odd number.') 27 | end 28 | 29 | if (length(size(M)) ~= 3) 30 | error('M must be 3-dimensional matrix.') 31 | end 32 | 33 | if (nargin ~= 4) 34 | error('Not enough input arguments'); 35 | end 36 | 37 | [h,w,p] = size(M); 38 | N = h*w; 39 | 40 | % Remove mean from the target 41 | M = hyperConvert2d(M); 42 | u = mean(M.').'; 43 | 44 | [Mpca,V,lambdaGlobal] = hyperPct(M,p); 45 | t_pct = V.'*(t-u); 46 | 47 | % Create map to get neighbors 48 | map = 1:N; 49 | map = reshape(map,h,w); 50 | 51 | R_hat = hyperCov(Mpca); 52 | G = inv(R_hat); 53 | 54 | results = zeros(h,w); 55 | s = floor(windowSize/2)+1; 56 | for k=s:(h-s) 57 | for kk=s:(w-s) 58 | midIdx = map(k,kk); 59 | neighborhoodIdx = map((k-s+1):(k+s-1),(kk-s+1):(kk+s-1)); 60 | 61 | Mlocal = M(:,neighborhoodIdx(:)); 62 | [~,~,lambdaLocal] = hyperPct(Mlocal,p); 63 | 64 | y = Mpca(:,midIdx); 65 | 66 | switch algorithm 67 | case 'sum' 68 | results(k,kk) = sum((t_pct.*y)./(lambdaLocal+lambdaGlobal)); 69 | case 'meanLocal' 70 | results(k,kk) = sum((t_pct.*y)/mean(lambdaLocal(:))); 71 | case 'meanGlobalLocal' 72 | results(k,kk) = sum((t_pct.*y)./((lambdaLocal+lambdaGlobal)/2)); 73 | otherwise 74 | error('Algorithm option unknown.'); 75 | end 76 | end 77 | end 78 | 79 | 80 | 81 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperRoc.m: -------------------------------------------------------------------------------- 1 | function [pd,fa] = hyperRoc(x) 2 | 3 | x = x(:); 4 | 5 | numTs = 100; 6 | pd = linspace(0,1,numTs); 7 | for k=1:numTs 8 | fa(k) = sum(x>=pd(k)); 9 | end 10 | 11 | N = length(x); 12 | fa = fa./N; 13 | pd = 1-pd; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperRxDetector.m: -------------------------------------------------------------------------------- 1 | function [result, sigma, sigmaInv] = hyperRxDetector(M) 2 | %HYPERRX RX anomaly detector 3 | % hyperRxDetector performs the RX anomaly detector 4 | % 5 | % Usage 6 | % [result] = hyperRxDetector(M) 7 | % Inputs 8 | % M - 2D data matrix (p x N) 9 | % Outputs 10 | % result - Detector output (1 x N) 11 | % sigma - Covariance matrix (p x p) 12 | % sigmaInv - Inverse of covariance matrix (p x p) 13 | 14 | % Remove the data mean 15 | [p, N] = size(M); 16 | mMean = mean(M, 2); 17 | M = M - repmat(mMean, 1, N); 18 | 19 | % Compute covariance matrix 20 | sigma = hyperCov(M); 21 | sigmaInv = inv(sigma); 22 | 23 | result = zeros(N, 1); 24 | for i=1:N 25 | result(i) = M(:,i).'*sigmaInv*M(:,i); 26 | end 27 | result = abs(result); 28 | 29 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperSam.m: -------------------------------------------------------------------------------- 1 | function [errRadians] = hyperSam(a, b) 2 | % HYPERSAM Computes the spectral angle error (in radians) between two vectors 3 | % 4 | % Usage 5 | % [errRadians] = hyperSam(a, b) 6 | % Inputs 7 | % a - Vector 1. 8 | % b - Vector 2. 9 | % Outputs 10 | % errRadians - angle between vectors a and b in radians 11 | 12 | [p,N] = size(a); 13 | errRadians = zeros(1,N); 14 | for k=1:N 15 | tmp = a(:,k); 16 | errRadians(k) = acos(dot(tmp, b)/ (norm(b) * norm(tmp))); 17 | end 18 | return; -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperSaveFigure.m: -------------------------------------------------------------------------------- 1 | function hyperSaveFigure(h, filename, fmt) 2 | % HYPERSAVEFIGURE Writes a figure to disk as an image. 3 | % 4 | % Usage 5 | % hyperSaveFigure(gcf, 'filename.png'); 6 | % Inputs 7 | % h - Handle to figure 8 | % filename - Filename for output file. Extension determines image type. 9 | % fmt - Format of output image. 'wysiwyg' or wysiwyp' for 'what you see 10 | % is what you get' and what 'you see is what you print' respectively. 11 | % Outputs 12 | % none 13 | 14 | if (nargin == 2) 15 | fmt = 'wysiwyg'; 16 | end 17 | 18 | fmt = lower(fmt); 19 | 20 | if strcmp(fmt, 'wysiwyp') 21 | saveas(h, filename); 22 | elseif strcmp(fmt, 'wysiwyg') 23 | set(h, 'Color',[1 1 1]); 24 | frame = getframe(h); 25 | [X,map] = frame2im(frame); 26 | imwrite(X ,filename); 27 | else 28 | error('Bad format string specified.'); 29 | end 30 | %print(h, '-dpng', filename); -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperSid.m: -------------------------------------------------------------------------------- 1 | function [ err ] = hyperSid( M, b ) 2 | % HYPERSID Computes the spectral information divergence between two vectors 3 | % 4 | % Usage 5 | % [err] = hyperSid(a, b) 6 | % Inputs 7 | % M - 2d matrix of data (p x N) 8 | % b - vector 2 9 | % Outputs 10 | % err - spectral information divergence between M and b 11 | % 12 | % References 13 | % C.-I Chang, "Spectral information divergence for hyperspectral image 14 | % analysis," IEEE 1999 International Geoscience and Remote Sensing Symp., 15 | % Hamburg, Germany, pp. 509-511, 28 June-2 July, 1999. 16 | 17 | [p, N] = size(M); 18 | err = zeros(1, N); 19 | for k=1:N 20 | err(k) = abs(sum(M(:,k).*log(M(:,k)./b)) + sum(b.*log(b./M(:,k)))); 21 | end 22 | err = 1./(err+eps); 23 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperSignedAce.m: -------------------------------------------------------------------------------- 1 | function [results] = hyperSignedAce(M, t) 2 | % TODO 3 | % HYPERACE Performs the adaptive cosin/coherent estimator algorithm 4 | % Performs the adaptive cosin/coherent estimator algorithm for target 5 | % detection. 6 | % 7 | % Usage 8 | % [results] = hyperAce(M, S) 9 | % Inputs 10 | % M - 2d matrix of HSI data (p x N) 11 | % S - 2d matrix of target endmembers (p x q) 12 | % Outputs 13 | % results - vector of detector output (N x 1) 14 | % 15 | % References 16 | % X Jin, S Paswater, H Cline. "A Comparative Study of Target Detection 17 | % Algorithms for Hyperspectral Imagery." SPIE Algorithms and Technologies 18 | % for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Vol 19 | % 7334. 2009. 20 | 21 | 22 | [p, N] = size(M); 23 | % Remove mean from data 24 | u = mean(M.').'; 25 | M = M - repmat(u, 1, N); 26 | t = t - u; 27 | 28 | R_hat = hyperCov(M); 29 | G = inv(R_hat); 30 | 31 | results = zeros(1, N); 32 | tmp = (t'*G*t); 33 | for k=1:N 34 | x = M(:,k); 35 | results(k) = ((x'*G*t)*abs(x'*G*t)) / (tmp*(x'*G*x)); 36 | end 37 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperUcls.m: -------------------------------------------------------------------------------- 1 | function [ W ] = hyperUcls( M, U ) 2 | %HYPERUCLS Unconstrained least squares 3 | % hyperUcls performs unconstrained least squares abundance estimation 4 | % 5 | % Usage 6 | % [ W ] = hyperUcls( M, U ) 7 | % Inputs 8 | % M - 2D data matrix (p x N) 9 | % U - 2D matrix of endmembers (p x q) 10 | % Outputs 11 | % W - Abundance maps (q x N) 12 | 13 | if (ndims(M) ~= 2) 14 | error('M must be a p x N matrix.'); 15 | end 16 | if (ndims(U) ~= 2) 17 | error('M must be a p x q matrix.'); 18 | end 19 | 20 | [p1, N] = size(M); 21 | [p2, q] = size(U); 22 | if (p1 ~= p2) 23 | error('M and U must have the same number of spectral bands.'); 24 | end 25 | 26 | Minv = pinv(U); 27 | W = zeros(q, N); 28 | for n1 = 1:N 29 | W(:, n1) = Minv*M(:, n1); 30 | end 31 | 32 | return; 33 | -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperVca.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/isaacgerg/matlabHyperspectralToolbox/7b5beb63831c69b3b2fb38431de06e6c868416cf/hyperspectralToolbox/hyperVca.m -------------------------------------------------------------------------------- /hyperspectralToolbox/hyperWhiten.m: -------------------------------------------------------------------------------- 1 | function [X_whitened, Aw, u] = hyperWhiten( X ) 2 | %HYPERWHITEN Whitens a data matrix 3 | % hyperWhiten whitens a data matrix by performing a transform upon it so 4 | % that diagonals of its covariance matrix are all unity. Whitening is 5 | % simply a coordinate rotation followed by a scaling factor. 6 | % 7 | % Usage 8 | % [X_whitened] = hyperWhiten( X ) 9 | % Inputs 10 | % X - 2D matrix (p x N) 11 | % Outputs 12 | % X_whitened - 2D matrix (p x N), now whitened 13 | % Aw - 2D whitening matrix. 14 | % u - Vector of data mean 15 | % 16 | % References 17 | % http://en.wikipedia.org/wiki/Whitening_transformation 18 | 19 | [p, N] = size(X); 20 | 21 | % Remove the data mean 22 | u = mean(X.').'; 23 | X = X - repmat(u, 1, N); 24 | 25 | % Compute covariance matrix 26 | sigma = hyperCov(X); 27 | % Compute SVD of covariance matrix to get eigenvectors/values 28 | % The columns of V are the eigenvectors of sigma. 29 | % Assume S is positive and U encodes the axis reflection information 30 | [U,S,V] = svd(sigma); 31 | Aw = inv(sqrt(S))* V.'; 32 | % Whiten the data 33 | X_whitened = Aw * X; 34 | 35 | return; --------------------------------------------------------------------------------