T. Gerhart, "Convex optimization techniques and their application in hyperspectral video processing", Thesis, December 2013.
J. Chang, T. Gerhart, " Applications of Low Rank and Sparse Matrix Decompositions in Hyperspectral Video Processing", Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, May 2016.
X. Cui, Y. Tian, L. Weng, Y. Yang, "Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition", ICGIP 2013, 2013.
S. Chen, S. Yang, K. Kalpakis, C. Chang, “Low-rank decomposition-based anomaly detection”, Proceedings of SPIE, Volume 8743, No. 87430, pages 1-24, 2013.
W. Sun, C. Liu, J. Li, Y. M. Lai, W. Li, “Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery", Journal of Applied Remote Sensing, 2014.
W. Li, Q. Du, “Collaborative representation for hyperspectral anomaly detection", IEEE Transactions on Geoscience and Remote Sensing, Volume 53, No. 3, pages 1463–1474, March 2015.
W. Wang, S. Li, H. Qi, B. Ayhan, C. Kwan, S. Vance, “Identify anomaly component by sparsity and low rank", Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015, pages 1-4, 2015.
Y. Qu, R. Guo, W. Wang, H. Qi, B. Ayhan, C. Kwan, S. Vance, “Anomaly detection in hyperspectral images through spectral unmixing and low rank decomposition", IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, pages 1855-1858, 2016.
Y. Qu, W. Wang, R. Guo, B. Ayhan, C. Kwan, S. Vance, H. Qi, “Hyperspectral anomaly detection through spectral unmixing and dictionary based low-rank decomposition,” IEEE Transactions on Geoscience and Remote Sensing, pages 4391-4405, 2018.
Y. Zhang, B. Du, L. Zhang, S. Wang, “A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection", IEEE Transactions on Geoscience Remote Sensing, Volume 54, No. 3, pages 1376–1389, March 2016.
Y. Xu, Z. Wu, J. Li, A. Plaza, Z. Wei, “Anomaly detection in hyperspectral images based on low-rank and sparse representation,” IEEE Transactions Geosciences and Remote Sensing, Volume 54, No. 4, pages 1990-2000, 2016.
Y. Xu, Z. Wu, J. Chanussot, M. Dalla Mura, A. L. Bertozzi, Z. Wei, "Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences", IEEE Transactions on Geoscience and Remote Sensing, Volume. 56, No. 3, pages 1680-1694, March 2018.
W. Sun, L. Tian, Y. Xu, B. Du, Q. Du, “A Randomized Subspace Learning based Anomaly Detector for Hyperspectral Imagery”, MDPI Remote Sensing, Volume 10, Issue 3, page 417, 2018.
F. Kucuk, B. Toreyin, F. Çelebi, "Sparse and low-rank matrix decomposition-based method for hyperspectral anomaly detection", Journal of Applied Remote Sensing, Volume 13, No. 1, page 014519, February 2019.
W. Sun, G. Yang, J. Li, D. Zhang, "Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection", Journal of Applied Remote Sensing, March 2018.
X. Ma, X. Zhang, X. Tang, H. Zhou, L. Jiao, “Hyperspectral anomaly detection based on low-rank representation with data-driven projection and dictionary construction", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 13, pages 2226-2239, 2020.
T. Cheng, B. Wang, “Graph and total variation regularized low-rank representation for hyperspectral anomaly detection", IEEE Transactions on Geoscience and Remote Sensing, Volume 58, No. 1, pages 391–406, 2020.
T. Cheng, B. Wang, “Total variation and sparsity regularized decomposition model with union dictionary for hyperspectral anomaly detection", IEEE Transactions on Geoscience and Remote Sensing, Volume 59, No. 2, pages 1472-1486, 2021.
H. Su, Z. Wu, A. Zhu, Q, Du , “Low rank and collaborative representation for hyperspectral anomaly detection via robust dictionary construction", ISPRS Journal of Photogrammetry and Remote Sensing, Volume 169, pages 195–211, 2020.
H. Cao, X. Shang, Y. Wang, M. Song, S. Chen, C. Chang, "Go Decomposition (GoDec) Approach to Finding Low Rank and Sparsity Matrices for Hyperspectral Target Detection", IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, pages 2807-2810, 2020.
C. Chang, H. Cao, S. Chen, X. Shang, C. Yu , M. Song, "Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection”, IEEE Transactions on Geoscience and Remote Sensing, 2020.
C. Chang, H. Cao, S. Chen, X. Shang, M. Song, C. Yu, “Orthogonal Subspace Projection-based GoDec for Low Rank and Sparsity Matrix Decomposition for Hyperspectral Anomaly Detection", IEEE Transactions on on Geoscience and Remote Sensing, Volume 59, No. 3, pages 2403-2429, 2021.
C. Chang, J. Chen, "Orthogonal Subspace Projection using Data Sphering and Low-Rank and Sparse Matrix Decomposition for Hyperspectral Target Detection", IEEE Transactions on Geoscience and Remote Sensing , 2021.
C. Chang, J. Chen, “OSP using Data Sphering and Low-Rank and Sparse Matrix Decomposition for Hyperspectral Target Detection", IEEE Transactions on Geoscience and Remote Sensing, volume 59, No. 10, pages 8704-8722, October 2021.
C. Chang, H. Cao, M. Song, “Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection", IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, Volume 14, pages 4915-4932, 2021.
C. Chang, "Constrained Energy Minimization Anomaly Detection for Hyperspectral Imagery via Dummy Variable Trick", IEEE Transactions on Geoscience and Remote Sensing, 2021.
S. Chen, C. Chang, X. Li, “Component decomposition analysis for hyperspectral target detection”, IEEE Transactions on Geoscience and Remote Sensing, 2021.
C. Chang, J. Chen, "Hyperspectral Anomaly Detection by Data Sphering and Sparsity Density Peaks", IEEE Transactions on Geoscience and Remote Sensing, 2021.
S. Feng, S. Tang, C. Zhao, Y. Cui, "A Hyperspectral Anomaly Detection Method Based on Low-Rank and Sparse Decomposition With Density Peak Guided Collaborative Representation", IEEE Transactions on Geoscience and Remote Sensing, 2021.
L. Li, W. Li, Q. Du, R. Tao, “Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection", IEEE Transactions on Cybernetics, Volume 51, No. 9, pages 4363–4372, 2020.
R. Feng, H. Li, L.Wang, Y. Zhong, L. Zhang, T. Zeng, “Local spatial constraint and total variation for hyperspectral anomaly detection", IEEE Transactions on Geoscience and Remote Sensing, pages 1–16, 2021.
Z. Wu, H. Su, X. Tao, L. Han, M. Paoletti, J. Haut, J. Plaza, A. Plaza, “Hyperspectral anomaly detection with relaxed collaborative representation", IEEE Transactions on Geoscience and Remote Sensing, Volume 60, pages 1-17, 2022.
M. Wang, D. Hong, B. Zhang, L. Ren, J. Yao, J. Chanussot, "Learning Double Subspace Representation for Joint Anomaly Detection and Noise Removal", IEEE Transactions on Geoscience and Remote Sensing, 2023.
L. Ren, L. Gao, M. Wang, X. Sun, J. Chanussot, "HADGSM: A Unified Nonconvex Framework for Hyperspectral Anomaly Detection", IEEE Transactions on Geoscience and Remote Sensing, Volume 62, pages 1-15, 2024.
L. Li, Z. Wu, B. Wang, "Hyperspectral Anomaly Detection via Merging Total Variation into Low-Rank Representation", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024.