A machine learning approach for finding hyperspectral endmembers

Abstract

A support vector algorithm for detecting endmembers in a hyperspectral image is introduced. It is a novel method for finding the spectral convexities in a high-dimensional space which addresses several limitations of previous endmember methods. A new approach for estimating the number of endmembers using rate-distortion theory is also presented. It is based upon the observation that the endmembers form a set of basis vectors for the hyperspectral datacube using the linear mixture model. The result is a fully-automatic method for endmember detection. Experimental results using the Cuprite datacube are presented.