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

Chia Hsiang Lin, Chong Yung Chi, Yu Hsiang Wang, Tsung Han Chan

研究成果: Article同行評審

34 引文 斯高帕斯(Scopus)

摘要

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 well-known blind HU criterion, advocated by Craig during the early 1990s, 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 kinds of algorithms may suffer from heavy simplex volume computations in numerical optimization, etc. In this paper, 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 an endmember identifiability analysis for its performance support. Without resorting to numerical optimization, the devised algorithm searches for the N(N-1) active data pixels via simple linear algebraic computations, accounting for its computational efficiency. Monte Carlo simulations and real data experiments are provided to demonstrate its superior efficacy over some benchmark Craig-criterion-based algorithms in both computational efficiency and estimation accuracy.

原文English
文章編號7355384
頁(從 - 到)1946-1961
頁數16
期刊IEEE Transactions on Signal Processing
64
發行號8
DOIs
出版狀態Published - 2016 四月 15

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

指紋 深入研究「A Fast Hyperplane-Based Minimum-Volume Enclosing Simplex Algorithm for Blind Hyperspectral Unmixing」主題。共同形成了獨特的指紋。

引用此