Hyperspectral Anomaly Detection Using Tensor Low-Rank Representation

Minghua Wang; Danfeng Hong; Yuanfeng Wu; Lianru Gao; Min Huang

doi:10.1007/978-981-19-6052-9_12

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Hyperspectral Anomaly Detection Using Tensor Low-Rank Representation

Minghua Wang Danfeng Hong Yuanfeng Wu Lianru Gao Min Huang

Year: 2022 Lecture notes in electrical engineering Pages: 127-132 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-19-6052-9_12

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Keywords:

Hyperspectral imaging Pattern recognition (psychology) Subspace topology Anomaly detection Rank (graph theory) Artificial intelligence Tensor (intrinsic definition) Representation (politics) Anomaly (physics) Computer science Sparse approximation Matrix norm Matrix (chemical analysis) Norm (philosophy) Mathematics Combinatorics Physics Chemistry Pure mathematics Eigenvalues and eigenvectors

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Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Hyperspectral Anomaly Detection Using Tensor Low-Rank Representation

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