Pointwise Mutual Information-Based Graph Laplacian Regularized Sparse Unmixing

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Kucuk S., YÜKSEL ERDEM S. E.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol.19, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2022
  • Doi Number: 10.1109/lgrs.2022.3143302
  • Journal Name: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Graph regularization, hyperspectral unmixing, pointwise mutual information (PMI), sparse regression, SPATIAL REGULARIZATION, REGRESSION
  • Hacettepe University Affiliated: Yes

Abstract

Sparse unmixing (SU) aims to express the observed image signatures as a linear combination of pure spectra known a priori and has become a very popular technique with promising results in analyzing hyperspectral images (HSIs) over the past ten years. In SU, utilizing the spatial-contextual information allows for more realistic abundance estimation. To make full use of the spatial-spectral information, in this letter, we propose a pointwise mutual information (PMI)-based graph Laplacian (GL) regularization for SU. Specifically, we construct the affinity matrices via PMI by modeling the association between neighboring image features through a statistical framework and then we use them in the GL regularizer. We also adopt a double reweighted l(1) norm minimization scheme to promote the sparsity of fractional abundances. Experimental results on simulated and real datasets prove the effectiveness of the proposed method and its superiority over competing algorithms in the literature.