LRR-Net: An Interpretable Deep Unfolding Network for Hyperspectral Anomaly Detection-Reference-Cited by-同舟云学术

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LRR-Net: An Interpretable Deep Unfolding Network 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:

Li Chenyu¹,Zhang Bing¹^ORCID,Hong Danfeng²^ORCID,Yao Jing²^ORCID,Chanussot Jocelyn³^ORCID

Affiliation:

1. School of Mathematics and Statistics, Southeast University, Nanjing, China

2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

3. INRIA, CNRS, Grenoble INP, GIPSA-Laboratory, Université Grenoble Alpes, Grenoble, France

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

MIAI at Grenoble Alpes

AXA Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10136197.pdf?arnumber=10136197

Reference50 articles.

1. Hyperspectral Anomaly Detection Using Deep Learning: A Review

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

3. Robust Recovery of Subspace Structures by Low-Rank Representation

4. Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction

5. Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images

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