Endmember finding and spectral unmixing using least-angle regression
2010
A new endmember finder and spectral unmixing algorithm based on the LARS/Lasso method for linear regression
is developed. The endmember finder is sequential; a single endmember is identified at first and further
endmembers which depend on the previous ones are found. The process terminates once a pre-determined number
of endmembers have been found, or when the modeling error has attained the noise floor. The unmixing
algorithm is a straightforward procedure that expresses each pixel as a linear combination of endmembers in a
physically meaningful way. This algorithm successfully unmixes simulated data, and shows promising results on
real hyperspectral images as well.
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