Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection-Reference-Cited by-同舟云学术

Learning Tensor Low-Rank Representation for Hyperspectral Anomaly Detection

Published:2023-01 Issue:1 Volume:53 Page:679-691
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Wang Minghua¹^ORCID,Wang Qiang²^ORCID,Hong Danfeng¹^ORCID,Roy Swalpa Kumar³^ORCID,Chanussot Jocelyn⁴^ORCID

Affiliation:

1. Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

2. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China

3. Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India

4. Université Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, Grenoble, France

Funder

National Natural Science Foundation of China

MIAI@Grenoble Alpes

AXA Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/9998320/09781337.pdf?arnumber=9781337

Reference64 articles.

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4. Joint subspace detection of hyperspectral targets

5. Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems

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