Structured low-rank representation learning for hyperspectral sparse unmixing

References

• Bing-Sheng, H., H. Yang, and S. L. Wang. 2000. “Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities.” Journal of Optimization Theory and Applications 106 (2): 337–356. https://doi.org/10.1023/A:1004603514434.
   Web of Science ®Google Scholar
• Bioucas-Dias, J. M., and M. A. T. Figueiredo. 2010. “Alternating Direction Algorithms for Constrained Sparse Regression: Application to Hyperspectral Unmixing.” In Proc. 2nd Workshop on Hyperspectral Image and Signal Process., Evol. Remote Sens., Reykjavik, Iceland, 1–4. IEEE.
   Google Scholar
• Bioucas-Dias, J. M., A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot. 2013. “Hyperspectral Remote Sensing Data Analysis and Future Challenges.” IEEE Geoscience and Remote Sensing Magazine 1 (2): 6–36. https://doi.org/10.1109/MGRS.2013.2244672.
   Web of Science ®Google Scholar
• Bioucas-Dias, J. M., A. Plaza, N. Dobigeon, M. Parente, D. Qian, P. D. Gader, and J. Chanussot. 2012. “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 5 (2): 354–379. https://doi.org/10.1109/JSTARS.2012.2194696.
   Web of Science ®Google Scholar
• Borsoi, R. A., T. Imbiriba, J. Carlos Moreira Bermudez, and C. Richard. 2019. “A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing.” IEEE Geoscience and Remote Sensing Letters 16 (4): 598–602. https://doi.org/10.1109/LGRS.2018.2878394.
   Web of Science ®Google Scholar
• Candes, E. J., M. B. Wakin, and S. P. Boyd. 2008. “Enhancing Sparsity by Reweighted L1 Minimization.” Journal of Fourier Analysis and Applications 14 (5–6): 877–905. https://doi.org/10.1007/s00041-008-9045-x.
   Web of Science ®Google Scholar
• Canyi, L., J. Feng, Y. Chen, W. Liu, Z. Lin, and S. Yan. 2019. “Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm.” IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (4): 925–938. https://doi.org/10.1109/TPAMI.2019.2891760.
   PubMed Web of Science ®Google Scholar
• Chang, Y., L. Yan, B. Chen, S. Zhong, and Y. Tian. 2021. “Hyperspectral Image Restoration: Where Does the Low-Rank Property Exist.” IEEE Transactions on Geoscience & Remote Sensing 59 (8): 6869–6884. https://doi.org/10.1109/TGRS.2020.3024623.
   Web of Science ®Google Scholar
• Fan, L., S. Zhang, B. Liang, C. Deng, X. Chenguang, and S. Wang. 2021. “Hyperspectral Sparse Unmixing with Spectral-Spatial Low-Rank Constraint.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14:6119–6130. https://doi.org/10.1109/JSTARS.2021.3086631.
   Web of Science ®Google Scholar
• Feng, X.-R., L. Heng-Chao, L. Jun, D. Qian, A. Plaza, and W. J. Emery. 2018. “Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization with Total Variation.” IEEE Transactions on Geoscience & Remote Sensing 56 (10): 6245–6257. https://doi.org/10.1109/TGRS.2018.2834567.
   Web of Science ®Google Scholar
• Fu, X., W.-K. Ma, J. M. Bioucas-Dias, and T.-H. Chan. 2016. “Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 54 (9): 5171–5184. https://doi.org/10.1109/TGRS.2016.2557340.
   Web of Science ®Google Scholar
• Goldstein, T., B. O’Donoghue, S. Setzer, and R. Baraniuk. 2014. “Fast Alternating Direction Optimization Methods.” SIAM Journal on Imaging Sciences 7 (3): 1588–1623. https://doi.org/10.1137/120896219.
   Web of Science ®Google Scholar
• Han, H., G. Wang, M. Wang, J. Miao, S. Guo, L. Chen, M. Zhang, and K. Guo. 2020. “Hyperspectral Unmixing via Nonconvex Sparse and Low-Rank Constraint.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13:5704–5718.
   Web of Science ®Google Scholar
• Hao, L., R. Feng, L. Wang, Y. Zhong, and L. Zhang. 2020. “Superpixel-Based Reweighted Low-Rank and Total Variation Sparse Unmixing for Hyperspectral Remote Sensing Imagery.” IEEE Transactions on Geoscience & Remote Sensing 59 (1): 629–647. https://doi.org/10.1109/TGRS.2020.2994260.
   Web of Science ®Google Scholar
• Harsanyi, J., and C.-I. Chang. 1994. “Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 32 (4): 779–785. https://doi.org/10.1109/36.298007.
   Web of Science ®Google Scholar
• Huang, J., T.-Z. Huang, L.-J. Deng, and X.-L. Zhao. 2019. “Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 57 (4): 2419–2438. https://doi.org/10.1109/TGRS.2018.2873326.
   Web of Science ®Google Scholar
• Huang, J., T.-Z. Huang, X.-L. Zhao, and L.-J. Deng. 2020. “Nonlocal Tensor-Based Sparse Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 59 (8): 6854–6868. https://doi.org/10.1109/TGRS.2020.3030233.
   Web of Science ®Google Scholar
• Imbiriba, T., R. Augusto Borsoi, and J. Carlos Moreira Bermudez. 2020. “Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 58 (3): 1833–1842. https://doi.org/10.1109/TGRS.2019.2949543.
   Web of Science ®Google Scholar
• Iordache, M.-D., J. M. Bioucas-Dias, and A. Plaza. 2011. “Sparse Unmixing of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 49 (6): 2014–2039. https://doi.org/10.1109/TGRS.2010.2098413.
   Web of Science ®Google Scholar
• Iordache, M.-D., J. M. Bioucas-Dias, and A. Plaza. 2012. “Total variation spatial regularization for sparse hyperspectral unmixing.” IEEE Transactions on Geoscience & Remote Sensing 50 (11): 4484–4502. https://doi.org/10.1109/TGRS.2012.2191590.
   Web of Science ®Google Scholar
• Iordache, M.-D., J. M. Bioucas-Dias, and A. Plaza. 2014. “Collaborative Sparse Regression for Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 52 (1): 341–354. https://doi.org/10.1109/TGRS.2013.2240001.
   Web of Science ®Google Scholar
• Jun, L., A. Agathos, D. Zaharie, J. M. Bioucas-Dias, A. J. Plaza, and L. Xia. 2015. “Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing.” IEEE Transactions on Geoscience & Remote Sensing 53 (9): 5067–5082. https://doi.org/10.1109/TGRS.2015.2417162.
   Web of Science ®Google Scholar
• Keshava, N., and J. F. Mustard. 2002. “Spectral unmixing.” IEEE Signal Processing Magazine 19 (1): 44–57. https://doi.org/10.1109/79.974727.
   Web of Science ®Google Scholar
• Kolda, T. G., and B. W. Bader. 2009. “Tensor decompositions and applications.” SIAM Review 51 (3): 455–500. https://doi.org/10.1137/07070111X.
   Web of Science ®Google Scholar
• Liu, J., P. Musialski, P. Wonka, and Y. Jieping. 2013. “Tensor Completion for Estimating Missing Values in Visual Data.” IEEE Transactions on Pattern Analysis & Machine Intelligence 35 (1): 208–220. https://doi.org/10.1109/TPAMI.2012.39.
   PubMed Web of Science ®Google Scholar
• Miao, L., and Q. Hairong. 2007. “Endmember Extraction from Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 45 (3): 765–777. https://doi.org/10.1109/TGRS.2006.888466.
   Web of Science ®Google Scholar
• Mingxi, M., X. Chenguang, J. Zhang, S. Wang, C. Deng, and Y. Wang. 2022. “Hyperspectral Sparse Unmixing Based on a Novel Adaptive Total Variation Regularization.” Infrared Physics & Technology 127:104362. https://doi.org/10.1016/j.infrared.2022.104362.
   Web of Science ®Google Scholar
• Nascimento, J. M. P., and J. M. B. Dias. 2005. “Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data.” IEEE Transactions on Geoscience & Remote Sensing 43 (4): 898–910. https://doi.org/10.1109/TGRS.2005.844293.
   Web of Science ®Google Scholar
• Parente, M., and M.-D. Iordache. 2021. “Sparse Unmixing of Hyperspectral Data: The Legacy of SUnSal.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 21–24. IEEE.
   Google Scholar
• Qian, Y., F. Xiong, S. Zeng, J. Zhou, and Y. Yan Tang. 2016. “Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 55 (3): 1776–1792. https://doi.org/10.1109/TGRS.2016.2633279.
   Web of Science ®Google Scholar
• Rudin, L. I., S. Osher, and E. Fatemi. 1992. “Nonlinear Total Variation Based Noise Removal Algorithms.” Physica D: Nonlinear Phenomena 60 (1–4): 259–268. https://doi.org/10.1016/0167-2789(92)90242-F.
   Web of Science ®Google Scholar
• Rui, W., L. Heng-Chao, A. Pizurica, L. Jun, A. Plaza, and W. J. Emery. 2017. “Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation.” IEEE Geoscience & Remote Sensing Letters 14 (7): 1146–1150. https://doi.org/10.1109/LGRS.2017.2700542.
   Web of Science ®Google Scholar
• Shen, X., and W. Bao. 2019. “Hyperspectral Endmember Extraction Using Spatially Weighted Simplex Strategy.” Remote Sensing 11 (18): 2147. https://doi.org/10.3390/rs11182147.
   Web of Science ®Google Scholar
• Shen, X., W. Bao, and Q. Kewen. 2020. “Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior Constrained Maximum Simplex Volume Approach.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 13: 1347–1361. https://doi.org/10.1109/JSTARS.2020.2981402.
   Web of Science ®Google Scholar
• Shen, X., W. Bao, and Q. Kewen. 2021. “Subspace-Based Preprocessing Module for Fast Hyperspectral Endmember Selection.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14: 3386–3402. https://doi.org/10.1109/JSTARS.2021.3065534.
   Web of Science ®Google Scholar
• Shen, X., L. Chen, H. Liu, X. Su, W. Wei, X. Zhu, and X. Zhou. 2023. “Efficient Hyperspectral Sparse Regression Unmixing with Multilayers.” IEEE Transactions on Geoscience and Remote Sensing.
   Google Scholar
• Shen, X., H. Liu, X. Zhang, K. Qin, and X. Zhou. 2022. “Superpixel-Guided Local Sparsity Prior for Hyperspectral Sparse Regression Unmixing.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. https://doi.org/10.1109/LGRS.2022.3218730.
   Web of Science ®Google Scholar
• Shi, Z., T. Shi, M. Zhou, and X. Xia. 2018. “Collaborative Sparse Hyperspectral Unmixing Using l0 Norm.” IEEE Transactions on Geoscience & Remote Sensing 56 (9): 5495–5508. https://doi.org/10.1109/TGRS.2018.2818703.
   Web of Science ®Google Scholar
• Sun, L., W. Feiyang, T. Zhan, W. Liu, J. Wang, and B. Jeon. 2020. “Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:1174–1188. https://doi.org/10.1109/JSTARS.2020.2980576.
   Web of Science ®Google Scholar
• Wang, J.-J., T.-Z. Huang, J. Huang, and L.-J. Deng. 2021. “A Two-Step Iterative Algorithm for Sparse Hyperspectral Unmixing via Total Variation.” Applied Mathematics Computation 401:126059. https://doi.org/10.1016/j.amc.2021.126059.
   Web of Science ®Google Scholar
• Wang, S. L., and L. Z. Liao. 2001. “Decomposition Method with a Variable Parameter for a Class of Monotone Variational Inequality Problems.” Journal of Optimization Theory and Applications 109 (2): 415–429. https://doi.org/10.1023/A:1017522623963.
   Web of Science ®Google Scholar
• Wang, M., Q. Wang, D. Hong, S. Kumar Roy, and J. Chanussot. 2022. “Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection.” IEEE Transactions on Cybernetics 53 (1): 679–691. https://doi.org/10.1109/TCYB.2022.3175771.
   PubMed Web of Science ®Google Scholar
• Wei, H., H. Zhang, and L. Zhang. 2017. “Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing.” IEEE Transactions on Geoscience & Remote Sensing 55 (7): 3909–3921. https://doi.org/10.1109/TGRS.2017.2683719.
   Web of Science ®Google Scholar
• Wei, H., H. Zhang, and L. Zhang. 2017. “Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing.” IEEE Transactions on Geoscience & Remote Sensing 55 (7): 3909–3921. https://doi.org/10.1109/TGRS.2017.2683719.
   Web of Science ®Google Scholar
• Winter, M. E. 1999. “N-FINDR: An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data.” In Imaging Spectrometry V, 266–276. Vol. 3753. International Society for Optics and Photonics.
   Google Scholar
• Xiong, F., J. Zhou, and Y. Qian. 2018. “Hyperspectral Imagery Denoising via Reweighed Sparse Low-Rank Nonnegative Tensor Factorization.” In 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 3219–3223. IEEE.
   Google Scholar
• Xue, S., W. Qiu, F. Liu, and X. Jin. 2018. “Low-rank tensor completion by truncated nuclear norm regularization.” In 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2600–2605. IEEE.
   Google Scholar
• Yang, L., X. Yuan, J.-L. Suo, D. J. Brady, and Q. Dai. 2019. “Rank Minimization for Snapshot Compressive Imaging.” IEEE Transactions on Pattern Analysis & Machine Intelligence 41 (12): 2990–3006. https://doi.org/10.1109/TPAMI.2018.2873587.
   PubMed Web of Science ®Google Scholar
• Yingying, X., F. Fang, and G. Zhang. 2015. “Similarity-Guided and $\ell_p$-Regularized Sparse Unmixing of Hyperspectral Data.” IEEE Geoscience & Remote Sensing Letters 12 (11): 2311–2315. https://doi.org/10.1109/LGRS.2015.2474744.
   Web of Science ®Google Scholar
• Yue, Y., and W. Sun. 2007. “Minimum Distance Constrained Non-Negative Matrix Factorization for the Endmember Extraction of Hyperspectral Images.” In MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, edited by Y. Wang, B. Lei, Y. Jing-Yu, J. Li, C. Wang, and Z. Liang-Pei, 679015. Vol. 6790. Wuhan China: International Society for Optics and Photonics.
   Google Scholar
• Zhang, Z., G. Ely, S. Aeron, N. Hao, and M. Kilmer. 2014. “Novel Methods for Multilinear Data Completion and de-Noising Based on Tensor-SVD.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 3842–3849.
   Google Scholar
• Zhang, S., L. Jun, L. Heng-Chao, C. Deng, and A. Plaza. 2018. “Spectral–Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 56 (6): 3265–3276. https://doi.org/10.1109/TGRS.2018.2797200.
   Web of Science ®Google Scholar
• Zhang, S., L. Jun, K. Liu, C. Deng, L. Liu, and A. Plaza. 2016. “Hyperspectral Unmixing Based on Local Collaborative Sparse Regression.” IEEE Geoscience & Remote Sensing Letters 13 (5): 631–635. https://doi.org/10.1109/LGRS.2016.2527782.
   Web of Science ®Google Scholar
• Zhao, X.-L., F. Wang, T.-Z. Huang, M. K. Ng, and R. J. Plemmons. 2013. “Deblurring and Sparse Unmixing for Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing: A Publication of the IEEE Geoscience and Remote Sensing Society 51 (7): 4045–4058. https://doi.org/10.1109/TGRS.2012.2227764.
   Web of Science ®Google Scholar
• Zheng, Y., W. Feiyang, H. Jae Shim, and L. Sun. 2019. “Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior.” Remote Sensing 11 (24): 2897. https://doi.org/10.3390/rs11242897.
   Web of Science ®Google Scholar
• Zheng, P., S. Hongjun, and D. Qian. 2021. “Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:1754–1767. https://doi.org/10.1109/JSTARS.2020.3048820.
   Web of Science ®Google Scholar