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    Nonlocal Band-Weighted Iterative Spectral Mixture Model for Hyperspectral Imagery Denoising

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    Fulltext not available
    Authors
    Yang, Longshan
    Xu, Linlin
    Peng, Junhuan
    Song, Yongze
    Wong, Alexander
    Clausi, David A
    Date
    2020
    Type
    Journal Article
    
    Metadata
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    Citation
    Yang, L. and Xu, L. and Peng, J. and Song, Y. and Wong, A. and Clausi, D.A. 2020. Nonlocal Band-Weighted Iterative Spectral Mixture Model for Hyperspectral Imagery Denoising. IEEE Transactions on Geoscience and Remote Sensing.
    Source Title
    IEEE Transactions on Geoscience and Remote Sensing
    DOI
    10.1109/TGRS.2020.2967587
    ISSN
    0196-2892
    Faculty
    Faculty of Humanities
    School
    School of Design and the Built Environment
    URI
    http://hdl.handle.net/20.500.11937/77950
    Collection
    • Curtin Research Publications
    Abstract

    Although efficient hyperspectral image (HSI) denoising relies on complete and accurate description and modeling the spatial-spectral signal in HSI, the current approaches do not fully account for key characteristics of HSI, i.e., the mixed spectra effect, the spatial nonstationarity effect, and noise variance heterogeneity effect. To address this issue, this article presents a linear spectral mixture model with nonlocal means constraint (LSMM-NLMC), with the following advantages. First, LSMM-NLMC can effectively learn the signal in mixed pixels in HSI by estimating clean endmembers and abundances for image restoration. Second, LSMM-NLMC can efficiently address nonstationary spatial correlation effect by imposing NLMC on the latent scene signal. Last, LSMM-NLMC provides accurate noise characterization by accounting for noise variance heterogeneity effect using a band-dependent noise model and a band-weighted Mahalanobis distance for similarity measurement. A novel optimization method based on the expectation-maximization (EM) algorithm and the purified means approach is used to efficiently solve the resulting maximum a posterior (MAP) problem. The experiments on both simulated and real HSI data sets demonstrate that the visual quality and denoising accuracy are significantly improved by the proposed LSMM-NLMC compared with previous methods.

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