Hyperspectral Image Unmixing via Alternating Projected Subgradients

Authors

A. Zymnis, S.-J. Kim, J. Skaf, M. Parente, S. Boyd

Year

2007

Conference Paper

Source

Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

Identifiers

DOI 10.1109/ACSSC.2007.4487406

Abstract¶

We consider the problem of factorizing a hyperspectral image into the product of two nonnegative matrices, which represent nonnegative bases for image spectra and mixing coefficients, respectively. This spectral unmixing problem is a nonconvex optimization problem, which is very difficult to solve exactly. We present a simple heuristic for approximately solving this problem based on the idea of alternating projected subgradient descent. Finally, we present the results of applying this method on the 1990 AVIRIS image of Cuprite, Nevada and show that our results are in agreement with similar studies on the same data.

Summary¶

Applies alternating projected subgradient steps to hyperspectral unmixing, estimating material spectra and abundances under simplex-like constraints. The paper is a compact example of large-scale convex optimization ideas entering remote-sensing inverse problems.