Convex cone volume analysis for finding endmembers in hyperspectral imagery

摘要

This paper presents a new approach, called convex cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be shown that finding the maximal volume of a convex cone in the original data space is equivalent to finding the maximal volume of a simplex in a hyperplane. As a result, the CCVA can take advantage of many recently developed fast computational algorithms developed for N-FINDR to derive their counterparts for CCVA.