Authors: Chein-I Chang; Wei Xiong; Shih-Yu Chen
Addresses: Information and Technology College, Dalian Maritime University, Dalian, China; School of Physics and Optoelectronic Engineering, Xidian University, Xian, China; Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; Department of Computer Science and Information Management, Providence University, Taichung, Taiwan ' Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA ' Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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.
Keywords: abundance non-negativity constraint; ANC; convex cone volume analysis; CCVA; convex cone projection; CCP; vertex component analysis; VCA; endmembers; hyperspectral imagery; data exploitation.
International Journal of Computational Science and Engineering, 2016 Vol.12 No.2/3, pp.209 - 236
Available online: 28 Apr 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article