Laser & Infrared, Volume. 55, Issue 4, 630(2025)

Approximate pure logarithmic low-rank and separable total variation regularization for hyperspectral unmixing

YANG Fei-xia1, LI Zheng1、*, DONG Xian-da1, and MA Fei2
Author Affiliations
  • 1School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China
  • 2School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
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    References(21)

    [1] [1] AZAR S G, MESHGINI S, BEHESHTI S, et al. Linear mixing model with scaled bundle dictionary for hyperspectral unmixing with spectral variability[J]. Signal Processing, 2021, 188: 108214.

    [2] [2] FERNANDEZ-BELTRAN R, PLA F, PLAZA A. Endmember extraction from hyperspectral imagery based on probabilistic tensor moments[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(12): 2120-2124.

    [3] [3] SONG D, SUN N, XU M, et al. Fast unmixing of noisy hyperspectral images based on vertex component analysis and singular spectrum analysis algorithms[J]. Canadian Journal of Remote Sensing, 2020, 46(1): 34-48.

    [4] [4] ZHANG Y, MA Y, DAI X, et al. Locality-constrained sparse representation for hyperspectral image classification[J]. Information Sciences, 2021, 546: 858-870.

    [5] [5] QI L, LI J, WANG Y, et al. Spectral-spatial-weighted multiview collaborative sparse unmixing for hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8766-8779.

    [6] [6] LI C, LIU Y, CHENG J, et al. Sparse unmixing of hyperspectral data with bandwise model[J]. Information Sciences, 2020, 512: 1424-1441.

    [7] [7] SU Y, LI J, PLAZA A, et al. DAEN: deep autoencoder networks for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4309-4321.

    [8] [8] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Total variation spatial regularization for sparse hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11): 4484-5402.

    [10] [10] LV X, WANG W, LIU H. Cluster-wise weighted nmf for hyperspectral images unmixing with imbalanced data[J]. Remote sensing, 2021, 13(2): 268-287.

    [11] [11] LI S, LI W, CAI L, et al. Subspace multi-regularized non-negative matrix factorization for hyperspectral unmixing[J]. Applied Intelligence, 2022, 53(10): 1-23.

    [12] [12] HU Z, NIE F, WANG R, et al. Low rank regularization: a review[J]. Neural Networks, 2021, 136(1).

    [14] [14] XIE Q, ZHAO Q, MENG D, et al. Multispectral images denoising by intrinsic tensor sparsity regularization[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 1692-1700.

    [15] [15] DIAN R, LI S. Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization[J]. IEEE Transactions on Image Processing, 2019, 28(10): 5135-5146.

    [16] [16] MA F, HUO S, YANG F. Graph-based logarithmic low-rank tensor decomposition for the fusion of remotely sensed images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 11271-11286.

    [17] [17] YAO J, HONG D, XU L, et al. Sparsity-enhanced convolutional decomposition: a novel tensor-based paradigm for blind hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14.

    [18] [18] LIN C H, CHI C Y, WANG Y H, et al. A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing[J]. IEEE Transactions on Signal Processing, 2016, 64(8): 1946-1961.

    [19] [19] LI J, AGATHOS A, ZAHARIE D, et al. Minimum volume simplex analysis: a fast algorithm for linear hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 5067-5082.

    [20] [20] SEVILLA J, MARTIN G, NASCIMENTO J M P. Parallel hyperspectral unmixing method via split augmented lagrangian on GPU[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 626-630.

    [21] [21] HONG D, YOKOYA N, CHANUSSOT J, et al. An augmented linear mixing model to address spectral variability for hyperspectral unmixing[J]. IEEE Transactions on Image Processing, 2019, 28(4): 1923-1938.

    [22] [22] ZKAN S, KAYA B, ESEN E, et al. EndNet: sparse AutoEncoder network for endmember extraction and hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1): 482-496.

    [23] [23] ZHUANG L, LIN C H, FIGUEIREDO M A, et al. Regularization parameter selection in minimum volume hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12): 9858-9877.

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    YANG Fei-xia, LI Zheng, DONG Xian-da, MA Fei. Approximate pure logarithmic low-rank and separable total variation regularization for hyperspectral unmixing[J]. Laser & Infrared, 2025, 55(4): 630

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    Paper Information

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    Received: Jul. 1, 2024

    Accepted: May. 29, 2025

    Published Online: May. 29, 2025

    The Author Email: LI Zheng (1595587774@qq.com)

    DOI:10.3969/j.issn.1001-5078.2025.04.022

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