Share Email Print
cover

Proceedings Paper

Hyperspectral image unmixing via bilinear generalized approximate message passing
Author(s): Jeremy Vila; Philip Schniter; Joseph Meola
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In particular, we employ the bilinear generalized approximate message passing algorithm (BiG-AMP), a recently proposed belief-propagation-based approach to matrix factorization, in a “turbo” framework that enables the exploitation of spectral coherence in the endmembers, as well as spatial coherence in the abundances. In conjunction, we propose an expectation- maximization (EM) technique that can be used to automatically tune the prior statistics assumed by turbo BiG-AMP. Numerical experiments on synthetic and real-world data confirm the state-of-the-art performance of our approach.

Paper Details

Date Published: 18 May 2013
PDF: 9 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430Y (18 May 2013); doi: 10.1117/12.2015859
Show Author Affiliations
Jeremy Vila, The Ohio State Univ. (United States)
Philip Schniter, The Ohio State Univ. (United States)
Joseph Meola, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top