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Shang, Wenting, Peng, Jiangjun, Wu, Zebin, Xu, Yang, Jouni, Mohamad, Mura, Mauro Dalla, Wei, Zhihui (2023) Hyperspectral Anomaly Detection via Sparsity of Core Tensor Under Gradient Domain. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-16 doi:10.1109/tgrs.2023.3297627

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Reference TypeJournal (article/letter/editorial)
TitleHyperspectral Anomaly Detection via Sparsity of Core Tensor Under Gradient Domain
JournalIEEE Transactions on Geoscience and Remote Sensing
AuthorsShang, WentingAuthor
Peng, JiangjunAuthor
Wu, ZebinAuthor
Xu, YangAuthor
Jouni, MohamadAuthor
Mura, Mauro DallaAuthor
Wei, ZhihuiAuthor
Year2023Volume61
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
DOIdoi:10.1109/tgrs.2023.3297627Search in ResearchGate
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Mindat Ref. ID16654872Long-form Identifiermindat:1:5:16654872:3
GUID0
Full ReferenceShang, Wenting, Peng, Jiangjun, Wu, Zebin, Xu, Yang, Jouni, Mohamad, Mura, Mauro Dalla, Wei, Zhihui (2023) Hyperspectral Anomaly Detection via Sparsity of Core Tensor Under Gradient Domain. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-16 doi:10.1109/tgrs.2023.3297627
Plain TextShang, Wenting, Peng, Jiangjun, Wu, Zebin, Xu, Yang, Jouni, Mohamad, Mura, Mauro Dalla, Wei, Zhihui (2023) Hyperspectral Anomaly Detection via Sparsity of Core Tensor Under Gradient Domain. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-16 doi:10.1109/tgrs.2023.3297627
In(2023) IEEE Transactions on Geoscience and Remote Sensing Vol. 61. Institute of Electrical and Electronics Engineers (IEEE)

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Jouni, Mohamad, Mura, Mauro Dalla, Drumetz, Lucas, Comon, Pierre (2023) MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 61. 1-21 doi:10.1109/tgrs.2023.3314218
Xu, Yang, Wu, Zebin, Chanussot, Jocelyn, Dalla Mura, Mauro, Bertozzi, Andrea L., Wei, Zhihui (2018) Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences. IEEE Transactions on Geoscience and Remote Sensing, 56 (3) 1680-1694 doi:10.1109/tgrs.2017.2766094
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