Hyperspectral Anomaly Detection via Structured Sparsity Plus Enhanced Low-Rankness-Reference-Cited by-同舟云学术

Hyperspectral Anomaly Detection via Structured Sparsity Plus Enhanced Low-Rankness

Published:2023 Issue: Volume:61 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhao Yin-Ping¹^ORCID,Li Hongyan²^ORCID,Chen Yongyong³^ORCID,Wang Zhen⁴^ORCID,Li Xuelong⁴^ORCID

Affiliation:

1. School of Software, Northwestern Polytechnical University, Xi’an, China

2. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China

3. School of Computer Science and Technology and the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Harbin Institute of Technology, Shenzhen, China

4. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Natural Science Basic Research Program of Shaanxi Province

Guangdong Natural Science Foundation

Shenzhen Science and Technology Program

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10148989.pdf?arnumber=10148989

Reference55 articles.

1. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery

2. Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

3. Sparse Representation for Computer Vision and Pattern Recognition

4. A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

5. Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters

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