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Volume20，Issue6，2016

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A Convex Cone Analysis Method for Endmember Selection of Multispectral and Hyperspectral Images Cover Article Back Cover Article Enhanced Publication AI Introduction
CHU Hai-feng1, ZHAI Zhong-min1, ZHAO Yin-di2, LI Ping-xiang1, ZHANG Liang-pei1
Issue 4, Pages: 460-467(2007)
Abstract：Convex Cone Analysis（CCA） method can be applied to endmember selection from multispectral and hyperspectral imagery.Each pixel on multispectral and hyperspectral imagery can also be regarded as one vector and the whole image is a convex cone formed by a number of nonnegative discrete vectors,so endmember selection is equivalent to search for the vertices of a convex cone.A method of automatically selecting best corners（vertices） is presented,which improves the traditional CCA method.Experiments on simulated data and real data verify the validity of CCA method.
Keywords：endmember selection;hyperspectral imagery;spectral unmixing
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