Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches

TL;DR

This paper introduces DANSER, a novel hyperspectral unmixing method that accounts for spectral signature mismatches by adjusting the spectral library, improving accuracy in practical remote sensing scenarios.

Contribution

The paper proposes a dictionary-adjusted nonconvex regression framework that effectively handles spectral mismatches, with a practical algorithm and a robust subspace approach for dictionary pruning.

Findings

DANSER outperforms existing methods in mitigating spectral mismatch effects.

The proposed approach demonstrates high accuracy in simulations and real-data experiments.

The method is computationally efficient for practical applications.

Abstract

The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects.