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

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu

Research output: Contribution to journalArticlepeer-review

734 Scopus citations

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 LMM, 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 and atmospheric effects) and instrumental configurations (e.g., sensor noise), and 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 the previous state-of-the-art methods.

Original languageEnglish
Article number8528557
Pages (from-to)1923-1938
Number of pages16
JournalIEEE Transactions on Image Processing
Volume28
Issue number4
DOIs
StatePublished - Apr 2019

Keywords

  • Alternating direction method of multipliers
  • low-coherent dictionary learning
  • remote sensing
  • spectral unmixing
  • spectral variability

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