Convex cone volume analysis for finding endmembers in hyperspectral imagery Online publication date: Thu, 28-Apr-2016
by Chein-I Chang; Wei Xiong; Shih-Yu Chen
International Journal of Computational Science and Engineering (IJCSE), Vol. 12, No. 2/3, 2016
Abstract: 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.
Online publication date: Thu, 28-Apr-2016
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