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Improving joint sparse hyperspectral unmixing by simultaneously clustering pixels according to their mixtures

Seyyedsalehi, S. F ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP43922.2022.9746552
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2022
  4. Abstract:
  5. In this paper we propose a novel hierarchical Bayesian model for sparse regression problem to use in semi-supervised hyperspectral unmixing which assumes the signal recorded in each hyperspectral pixel is a linear combination of members of the spectral library contaminated by an additive Gaussian noise. To effectively utilizing the spatial correlation between neighboring pixels during the unmixing process, we exploit a Markov random field to simultaneously group pixels to clusters which are associated to regions with homogeneous mixtures in a natural scene. We assume Sparse fractional abundances of members of a cluster to be generated from an exponential distribution with the same rate parameter. We show that our method is able to detect unconnected regions which have similar mixtures. Experiments on synthetic and real hyperspectral images confirm the superiority of the proposed method compared to alternatives. © 2022 IEEE
  6. Keywords:
  7. Markov random field ; Bayesian networks ; Gaussian noise (electronic) ; Markov processes ; Mixtures ; Remote sensing ; Spectroscopy ; Clustering pixels ; Hierarchical Bayesian modeling ; HyperSpectral ; Hyperspectral unmixing ; Joint sparse regression ; Markov Random Fields ; Semi-supervised ; Semi-supervised hyperspectral unmixing ; Sparse regression ; Spatial correlations ; Pixels
  8. Source: 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, 23 May 2022 through 27 May 2022 ; Volume 2022-May , 2022 , Pages 5088-5092 ; 15206149 (ISSN); 9781665405409 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/9746552