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dc.contributor.authorYang, Longshan
dc.contributor.authorXu, Linlin
dc.contributor.authorPeng, Junhuan
dc.contributor.authorSong, Yongze
dc.contributor.authorWong, Alexander
dc.contributor.authorClausi, David A
dc.identifier.citationYang, 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.

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.

dc.titleNonlocal Band-Weighted Iterative Spectral Mixture Model for Hyperspectral Imagery Denoising
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Geoscience and Remote Sensing
curtin.departmentSchool of Design and the Built Environment
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Humanities
curtin.contributor.orcidSong, Yongze [0000-0003-3420-9622]
curtin.contributor.researcheridSong, Yongze [F-1940-2018]
curtin.contributor.scopusauthoridSong, Yongze [57200073199]

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