unmixing multispectral imaging data in matlab

Multispectral imaging data often contains mixed spectral signatures from different sources, making it difficult to analyze and interpret. Unmixing refers to the process of separating mixed spectra into their constituent parts. This can be done in MATLAB using a variety of techniques, including linear unmixing, non-negative matrix factorization, and sparsity-based approaches.

Here's an example code for linear unmixing in MATLAB, assuming you have an n x m x p array (data cube) called 'data' containing the multispectral data, an array of p x q called 'endmembers' containing the spectral signatures of the end members, and an n x m x q array called 'abundances' containing the abundances of each end member in each pixel of the data:

main.m
% Reshape data and endmembers into 2D matrices
data_2D = reshape(data, [n*m, p]);
endmembers_2D = reshape(endmembers, [p, q]);

% Perform linear unmixing
abundances_2D = data_2D / endmembers_2D;

% Reshape abundances back into 3D array
abundances = reshape(abundances_2D, [n, m, q]);
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This code calculates the abundances of each end member in each pixel by solving the linear system of equations data = abundances * endmembers. The resulting 'abundances' array can be further analyzed and visualized to gain insights into the multispectral data.