A Fast Hyperplane-Based Minimum-Volume Enclosing Simplex Algorithm for Blind Hyperspectral Unmixing

—Hyperspectral unmixing (HU) is a crucial signal processing procedure to identify the underlying materials (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. A wellknown blind HU criterion, advocated by Craig in early 1990’s, considers the vertices of the minimum-volume enclosing simplex of the data cloud as good endmember estimates, and it has been empirically and theoretically found effective even in the scenario of no pure pixels. However, such kind of algorithms may suffer from heavy simplex volume computations in numerical optimization, etc. In this work, without involving any simplex volume computations, by exploiting a convex geometry fact that a simplest simplex of N vertices can be defined by N associated hyperplanes, we propose a fast blind HU algorithm, for which each of the N hyperplanes associated with the Craig’s simplex of N vertices is constructed from N − 1 affinely independent data pixels, together with ...

• Type: Journal
• Year: 2016
• Where: TSP
• Authors: Chia-Hsiang Lin, Chong-Yung Chi, Yu-Hsiang Wang, Tsung-Han Chan
• Keywords: TSP 2016