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Hyperspectral imagery collected from airborne or satellite sources inevitablysuffers from spectral variability, making it difficult for spectral unmixing toaccurately estimate abundance maps. The classical unmixing model, the linearmixing model (LMM), generally fails to handle this sticky issue effectively. Tothis end, we propose a novel spectral mixture model, called the augmentedlinear mixing model (ALMM), to address spectral variability by applying adata-driven learning strategy in inverse problems of hyperspectral unmixing.The proposed approach models the main spectral variability (i.e., scalingfactors) generated by variations in illumination or typography separately bymeans of the endmember dictionary. It then models other spectral variabilitiescaused by environmental conditions (e.g., local temperature and humidity,atmospheric effects) and instrumental configurations (e.g., sensor noise), aswell as material nonlinear mixing effects, by introducing a spectralvariability dictionary. To effectively run the data-driven learning strategy,we also propose a reasonable prior knowledge for the spectral variabilitydictionary, whose atoms are assumed to be low-coherent with spectral signaturesof endmembers, which leads to a well-known low coherence dictionary learningproblem. Thus, a dictionary learning technique is embedded in the framework ofspectral unmixing so that the algorithm can learn the spectral variabilitydictionary and estimate the abundance maps simultaneously. Extensiveexperiments on synthetic and real datasets are performed to demonstrate thesuperiority and effectiveness of the proposed method in comparison withprevious state-of-the-art methods.

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