# HyperSpectralToolbox

**Repository Path**: xlk159357/HyperSpectralToolbox

## Basic Information

- **Project Name**: HyperSpectralToolbox
- **Description**: No description available
- **Primary Language**: Matlab
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-30
- **Last Updated**: 2021-05-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

### Welcome to my Project ###

For better viewing, visit <http://davidkun.github.io/HyperSpectralToolbox/>  

Originally created by [Isaac Gerg](http://www.gergltd.com/home/)  
Under GNU General Public License version 2.0 (GPLv2).  
[Visit SourceForge](http://sourceforge.net/apps/mediawiki/matlabhyperspec/index.php?title=Main_Page)

**Dependencies**  
FastICA ([from my Github repo](https://github.com/davidkun/FastICA) or [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](https://raw.githubusercontent.com/davidkun/HyperSpectralToolbox/master/figures/sampleSlice.png)  
_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](https://raw.githubusercontent.com/davidkun/HyperSpectralToolbox/master/figures/truecolor.png)  
_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](https://raw.githubusercontent.com/davidkun/HyperSpectralToolbox/master/figures/targets_spectra.png)  
_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](https://raw.githubusercontent.com/davidkun/HyperSpectralToolbox/master/figures/endmmbrs-nfindr.png)  
_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](https://raw.githubusercontent.com/davidkun/HyperSpectralToolbox/master/figures/abund-nfindr-1.png)  
_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)  
***