Matlab Hyperspectral Toolbox

Copyright 2008-2012 Isaac Gerg

A Note on Notation

Hyperspectral data is often expressed many ways to better describe the mathematical handling of the data; mainly as a vector of pixels when referring to the data in a space or a matrix of pixels when referring to data as an image.

For consistency, a common notation is defined to differentiate these concepts clearly. Hyperspectral data examined like an image will be defined as a matrix Mm x n x p of dimension m x n x p where m is defined as the number of rows in the image, n is defined as the number of columns in the image, and p is defined as the number of bands in the image. Therefore, a single element of such an image will be accessed using Mi,j,k and a single pixel of an image will be accessed using Mi,j,: Hyperspectral data formed as a vector of vectors (i.e. 2D matrix) is defined as M(mn) x p of dimension (mn) x p

A single element is accessed using Mi,j and a single pixel is accessed using M:,j . Notice the multi-element notation is consistent with MatlabTM this is intentional.

The list below provides a summary of the notation convention used throughout this code.

M Data matrix. Defined as an image of spectral signatures or vectors: Mmxnxp. Or, defined as a long vector of spectral signatures: M(mn) x p

N The total number of pixels. For example N = m x n.

m Number of rows in the image.

n Number of columns in the image.

p Number of bands.

q Number of classes / endmembers.

U Matrix of endmembers. Each column of the matrix represents an endmember vector.

b Observation vector; a single pixel.

x Weight vector. A matrix of weight vectors forms an abundance map.

Dependencies FastICA - http://www.cis.hut.fi/projects/ica/fastica/code/dlcode.shtml

Functions

Reading/Writing Data Files

• hyperReadAvirisRfl - Reads AVIRIS .rfl files
• hyperReadAvirisSpc - Read AVIRIS .spc files
• hyperReadAsd - Reads ASD Fieldspec files. (.asd, .000, etc)

Data Formatting

• hyperConvert2D - Converts data from a 3D HSI data cube to a 2D matrix
• hyperConvert3D - Converts data from a 2D matrix to a 3D HSI data cube
• hyperNormalize - Normalizes data to be in range of [0,1]
• hyperConvert2Jet - Converts a 2D matrix to jet colormap values
• hyperResample - Resamples hyperspectral data to new wavelength set

Unmixing

• hyperAtgp - ATGP algorithm
• hyperIcaEea - ICA-Endmember Extraction Algorithm
• hyperIcaComponentScores - Computes ICA component scores for relevance
• hyperVca - Vertex Component Analysis
• hyperPPI - Pixel Purity Index

Target Detection

• hyperACE - Adaptive cosine/coherent estimator
• hyperGLRT - Generalized liklihood ratio test
• hyperHUD - Hybrid instructured detector
• hyperAMSD - Adaptive matched subspace detector
• hyperMatchedFilter - Matched filter
• hyperOsp - Orthogonal subspace projection
• hyperCem - Constrained energy minimization
• hyperPlmf - PCA local matched filter
• hyperRmf - Regularized match filter

Material Count Estimation

• hyperHfcVd - Computes virtual dimensionality (VD) using HFC method

Data Conditioning

• hyperPct - Pricipal component transform
• hyperMnf - Minimum noise fraction
• hyperDestreak - Destreaking algorithm

Abundance Map Generation

• hyperUcls - Unconstrained least squares
• hyperNnls - Non-negative least squares
• hyperFcls - Fully constrains least squares

Spectral Measuring

• hyperSam - Spectral Angle Mapper
• hyperSid - Spectral Information Divergence
• hyperNormXCorr - Normalized Cross Correlation

Miscellaneous

• hyperMax2d - Finds the max value and corresonding position in a matrx

Sensor Specific

• hyperGetHymapWavelengthsNm - Returns list of Hymap wavelengths

Statistics

• hyperCov - Sample covariance matrix estimator
• hyperCorr - Sample autocorrelation matrix estimator

Demos

• hyperDemo - General toolbox usage
• hyperDemo_detectors - Target detection algorithms
• hyperDemo_RIT_data - RIT target detection blind test
• hyperDemo_ASD_reader - Reads ASD Fieldspec files