Convex cone volume analysis for finding endmembers in hyperspectral imagery. (2016)

• Record Type: Journal Article
• Authors: Chang, Chein-I; Xiong, Wei; Chen, Shih-Yu
• Is Part Of: International journal of computational science and engineering. Volume 12:Number 2/3(2016)
• Page Start: 209
• Page End: 236
• Publication Date: 2016
• Subjects: abundance non-negativity constraint -- ANC -- convex cone volume analysis -- CCVA -- convex cone projection -- CCP -- vertex component analysis -- VCA -- endmembers -- hyperspectral imagery -- data exploitation; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.