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

TL;DR

This paper introduces a fast, hyperplane-based algorithm for blind hyperspectral unmixing that avoids complex volume computations, significantly improving efficiency and accuracy over traditional methods.

Contribution

It proposes a novel hyperplane-based approach that eliminates the need for volume calculations, enabling rapid and accurate endmember estimation in hyperspectral data.

Findings

Outperforms benchmark algorithms in computational speed.

Achieves higher estimation accuracy in experiments.

Demonstrates effectiveness on real hyperspectral datasets.

Abstract

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 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 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.