An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

TL;DR

This paper introduces the augmented linear mixing model (ALMM), a novel hyperspectral unmixing approach that effectively models spectral variability using a data-driven dictionary learning strategy, outperforming existing methods.

Contribution

The paper proposes ALMM, which models spectral variability with a learned dictionary and integrates it into hyperspectral unmixing, addressing limitations of the classical linear mixing model.

Findings

ALMM outperforms state-of-the-art methods on synthetic datasets.

ALMM effectively models various sources of spectral variability.

The approach demonstrates superior accuracy on real hyperspectral data.

Abstract

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.