毕业设计&课设-Sourceforge派生的HyperSpectral Matlab工具箱.zip

### HyperSpectral Toolbox ### For better viewing, visit <http://davidkun.github.io/HyperSpectralToolbox/> Originally created by [Isaac Gerg](http://www.gergltd.com/home/) and maintained by him [here](https://github.com/isaacgerg/matlabHyperspectralToolbox). # Note # **This repository is no longer being maintained or updated.** Isaac (the original creator of 99% of this work) has [his own GitHub repository](https://github.com/isaacgerg/matlabHyperspectralToolbox) with this work and intends to actively maintain / update it there. _Please use that repository and see his BibTeX citation for correctly referencing the software in your research._ **Dependencies** FastICA -- [from Aalto University](http://research.ics.aalto.fi/ica/fastica/code/dlcode.shtml)) ## Setup ## In the terminal type: cd ~/path-to-directory git clone https://github.com/davidkun/HyperSpectralToolbox.git git clone https://github.com/davidkun/FastICA.git Open Matlab. The default directory should contain a file `startup.m`. If not, create it: % in Matlab command window uPath = userpath; cd(uPath(1:end-1)); % removes trailing colon edit startup.m % may ask if you'd like to create it; click Yes Add the following code to it (make sure to modify `path-to-directory` so it matches the actual path): addtopath('~/path-to-directory/FastICA', ... '~/path-to-directory/HyperSpectralToolbox/functions', ... '~/path-to-directory/HyperSpectralToolbox/newFunctions'); You're ready to go now! Check out the demo files `hyperDemo.m` in `functions/` and `hyperDemo2.m` in `newFunctions/` to learn how to use the toolbox, or see the examples further down this page. *** [Back to top](https://github.com/davidkun/HyperSpectralToolbox#welcome-to-my-project) *** ## Description ## The open source Matlab Hyperspectral Toolbox is a Matlab toolbox containing various hyperspectral exploitation algorithms. The toolbox is meant to be a concise repository of current state-of-the-art exploitation algorithms for learning and research purposes. The toolbox includes functions for: * **Target detection** * Constrained Energy Minimization (CEM) * Orthogonal Subspace Projection (OSP) * Generalized Likelihood Ratio Test (GLRT) * Adaptive Cosine/Coherent Estimator (ACE) * Adaptive Matched Subspace Detector (AMSD) * **Endmember Finders** * Automatic Target Generation Procedure (ATGP) * Independent component analysis - endmember extraction algorithm (ICA-EEA) * **Material abundance map (MAM) generation** * **Spectral Comparison** * Spectral angle mapper (SAM) * Spectral information divergence (SID) * Normalize cross correlation * **Anomaly Detectors** * Reed-Xiaoli Detector (RX) * **Least Square Solvers** (for abundance map estimation) * Fully-constrained least squares (FCLS) * Non negative least squares (NNLS) * **Material Count Estimation** * HFC virtual dimensionality (VD) for material count estimate * **Automated processing** * **Change detection** * **Visualization** * **Reading / writing files** (.rfl, .asd, ect) *** [Back to top](https://github.com/davidkun/HyperSpectralToolbox#welcome-to-my-project) *** ## Examples ## Download the Cuprite, Nevada hyperspectral image (HSI) from [here](http://aviris.jpl.nasa.gov/data/free_data.html). This will contain reflectance data and a .spc file with the spectral bands. The following samples of code are from `hyperDemo2.m`. Show a 'slice' of the HSI: slice = hyperReadAvirisRfl(rflFile, [1 512], [1 614], [bndnum bndnum]); figure; imagesc(slice); axis image; colormap(gray);  _Figure 1: 1997 AVIRIS flight over Cuprite, NV_ View an enhanced truecolor composite of the HSI: tColor = hyperTruecolor(rflFile, 512, 614, 224, rgbBands, 'stretchlim'); figure; imagesc(tColor); axis image  _Figure 2: Truecolor composite from RGB bands_ Plot the spectral signatures of 20 random pixels in order to determine which bands are greatly affected by water absorption and/or have a low signal-to-noise ratio (SNR):  _Figure 3: Pre-processing: removal of poor spectral bands from original HSI_ Using the resampled HSI cube, perform an endmember extraction algorithm, for example, the N-FINDR algorithm: Unfindr = hyperNfindr(M2d, q); figure; plot(lambdasNm, Unfindr, '.'); grid on;  _Figure 4: Endmember signatures estimated by PPI_ Generate abundance maps using the non-negative constrained least squares method for each extracted endmember signature, for example: abundanceMaps = hyperNnls(M2d, Uppi); abundanceMaps = hyperConvert3d(abundanceMaps, h, w, q); figure; imagesc(abundanceMaps(:,:,1)); colorbar; axis image;  _Figure 5: Abundance map from first N-FINDR-recovered endmember_ These are just a few features of the Hyperspectral Toolbox. *** [Back to top](https://github.com/davidkun/HyperSpectralToolbox#welcome-to-my-project) *** ### Algorithms to be added (requested by Dr. Gerg): ### (Joint) Affine Matched filter Generalization of matched filter which includes signature statistics RAF-SAM, an improvement to SAM from: Improving the Classification Precision of Spectral Angle Mapper ELM for radiance to reflectance conversion - http://www.cis.rit.edu/files/197_SPIE_2005_Grimm.pdf Covariance matrix inversion methods (e.g. Dominant Mode Rejection) Quadratic Detector SMACC - http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=844250 ~~AMEE - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1046852~~ ~~N-FINDR - http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=994814~~ Fast PPI - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1576691 ~~Joshua Broaderwater's hybrid detectors (HUD, etc)~~ Variations on ACE - e.g. adaptive covariance estimated ACE, etc *** [Back to top](https://github.com/davidkun/HyperSpectralToolbox#welcome-to-my-project) ***