Laser & Infrared, Volume. 54, Issue 4, 642(2024)
Hyperspectral unmixing based on low rank orthogonal priors for spectral variability
[1] [1] Borsoi R A, Imbiriba T, Bermudez J C M. A data dependent multiscale model for hyperspectral unmixing with spectral variability[J]. IEEE Transactions on Image Processing, 2020, 29: 3638-3651.
[3] [3] Borsoi R A, Imbiriba T, Bermudez J C M, et al. Spectral variability in hyperspectral data unmixing: a comprehensive review[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(4): 223-270.
[4] [4] Shi S, Zhang L, Altmann Y, et al. Deep generative model for spatial-spectral unmixing with multiple endmember priors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
[5] [5] Hong D, Gao L, Yao J, et al. Endmember-guided unmixing network (EGU-Net): a general deep learning framework for self-supervised hyperspectral unmixing[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(11): 6518-6531.
[6] [6] Schmidt M N, Winther O, Hansen L K. Bayesian non-negative matrix factorization[C]//Independent Component Analysis and Signal Separation: 8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009.
[7] [7] Arngren M, Schmidt M N, Larsen J. Unmixing of hyperspectral images using bayesian non-negative matrix factorization with volume prior[J]. Journal of Signal Processing Systems, 2011, 65: 479-496.
[8] [8] Fang Y, Wang Y, Xu L, et al. BCUN: bayesian fully convolutional neural network for hyperspectral spectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-14.
[9] [9] Zare A, Ho K C. Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing[J]. IEEE Signal Processing Magazine, 2013, 31(1): 95-104.
[10] [10] Ghaffari O, ValadanZoej M J, Mokhtarzade M. Reducing the effect of the endmembers' spectral variability by selecting the optimal spectral bands[J]. Remote Sensing, 2017, 9(9): 884.
[11] [11] Thouvenin P A, Dobigeon N, Tourneret J Y. Hyperspectral unmixing with spectral variability using a perturbed linear mixing model[J]. IEEE Transactions on Signal Processing, 2015, 64(2): 525-538.
[12] [12] Drumetz L, Veganzones M A, Henrot S, et al. Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability[J]. IEEE Transactions on Image Processing, 2016, 25(8): 3890-3905.
[13] [13] 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, 2018, 28(4): 1923-1938.
[14] [14] Zhang G, Mei S, Xie B, et al. Spectral variability augmented sparse unmixing of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-13.
[15] [15] Imbiriba T, Borsoi R A, Bermudez J C M. Generalized linear mixing model accounting for endmember variability[C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018: 1862-1866.
[16] [16] Hu Z, Nie F, Wang R, et al. Low rank regularization: a review[J]. Neural Networks, 2021, 136: 218-232.
[18] [18] Chi C Y, Li W C, Lin C H. Convex optimization for signal processing and communications: from fundamentals to applications[M]. CRC Press, 2017.
[19] [19] 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.
[20] [20] 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.
[21] [21] Li H, Borsoi R A, Imbiriba T, et al. Model-based deep autoencoder networks for nonlinear hyperspectral unmixing[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[22] [22] Chan T H, Chi C Y, Huang Y M, et al. A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing[J]. IEEE Transactions on Signal Processing, 2009, 57(11): 4418-4432.
[23] [23] Borsoi R A, Imbiriba T, Bermudez J C M. Deep generative endmember modeling: an application to unsupervised spectral unmixing[J]. IEEE Transactions on Computational Imaging, 2019, 6: 374-384.
[24] [24] Clark R N, Swayze G A, Livo K E, et al. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems[J]. Journal of Geophysical Research: Planets, 2003, 108(E12).
[25] [25] Hong D, Zhu X X. SULoRA: subspace unmixing with low-rank attribute embedding for hyperspectral data analysis[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(6): 1351-1363.
[26] [26] Bioucas-Dias J M, Nascimento J M P. Hyperspectral subspace identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8): 2435-2445.
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MA Fei, LI Shu-xue, YANG Fei-xia, XU Guang-xian. Hyperspectral unmixing based on low rank orthogonal priors for spectral variability[J]. Laser & Infrared, 2024, 54(4): 642
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Received: Apr. 26, 2023
Accepted: May. 21, 2025
Published Online: May. 21, 2025
The Author Email: LI Shu-xue (17852270103@163.com)