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2009
IEEE

Convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing

10 years 4 months ago
Convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing
Abstract—Hyperspectral unmixing aims at identifying the hidden spectral signatures (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. Many existing hyperspectral unmixing algorithms were developed under a commonly used assumption that pure pixels exist. However, the pure-pixel assumption may be seriously violated for highly mixed data. Based on intuitive grounds, Craig reported an unmixing criterion without requiring the pure-pixel assumption, which estimates the endmembers by vertices of a minimum-volume simplex enclosing all the observed pixels. In this paper, we incorporate convex analysis and Craig’s criterion to develop a minimum-volume enclosing simplex (MVES) formulation for hyperspectral unmixing. A cyclic minimization algorithm for approximating the MVES problem is developed using linear programs (LPs), which can be practically implemented by readily available LP solvers. We also provide a nonheuristic guarantee of our...
Tsung-Han Chan, Chong-Yung Chi, Yu-Min Huang, Wing
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Tsung-Han Chan, Chong-Yung Chi, Yu-Min Huang, Wing-Kin Ma
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