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

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Generalized Nonconvex Low-Rank Tensor Representation for Hyperspectral Anomaly Detection

Published:2023 Issue: Volume:61 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Qin Hao¹,Shen Qiangqiang²^ORCID,Zeng Haijin³,Chen Yongyong¹^ORCID,Lu Guangming¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

2. School of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Guangdong, Shenzhen, China

3. Image Processing and Interpretation’ imec Research Group in Ghent University, Ghent, Belgium

Funder

National Natural Science Foundation of China

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/ielx7/36/10006360/10272243.pdf?arnumber=10272243

Reference60 articles.

1. Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data

2. Reduced rank regression via adaptive nuclear norm penalization

3. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution

4. Nearly unbiased variable selection under minimax concave penalty

5. Multiple-Window Anomaly Detection for Hyperspectral Imagery

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary;Remote Sensing;2024-05-21

2. Memory-Augmented Autoencoder With Adaptive Reconstruction and Sample Attribution Mining for Hyperspectral Anomaly Detection;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Hyperspectral Anomaly Detection via MERA Decomposition and Enhanced Total Variation Regularization;IEEE Transactions on Geoscience and Remote Sensing;2024

4. DSRNet: Diagonal Subsampling Reconstruction Network for Hyperspectral Anomaly Detection;IEEE Transactions on Geoscience and Remote Sensing;2024

5. Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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