Hyperspectral Anomaly Detection With Relaxed Collaborative Representation-Reference-Cited by-同舟云学术

Hyperspectral Anomaly Detection With Relaxed Collaborative Representation

Published:2022 Issue: Volume:60 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wu Zhaoyue¹^ORCID,Su Hongjun²,Tao Xuanwen¹^ORCID,Han Lirong¹^ORCID,Paoletti Mercedes E.¹^ORCID,Haut Juan M.¹^ORCID,Plaza Javier¹^ORCID,Plaza Antonio¹^ORCID

Affiliation:

1. Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, University of Extremadura, Cáceres, Spain

2. School of Earth Sciences and Engineering, Hohai University, Nanjing, China

Funder

Consejería de Economía, Ciencia y Agenda Digital of the Junta de Extremadura and the European Regional Development Fund (ERDF) of the European Union

Spanish Ministerio de Ciencia e Innovacion

European Union’s Horizon 2020 Research and Innovation Program

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09826842.pdf?arnumber=9826842

Reference56 articles.

1. Hyperspectral Anomaly Detection Via Dual Collaborative Representation

2. A Hyperspectral Anomaly Detection Method Based on Low-Rank and Sparse Decomposition With Density Peak Guided Collaborative Representation

3. Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation

4. Low-rank tensor decomposition based anomaly detection for hyperspectral imagery

5. Hyperspectral Anomaly Detection With Total Variation Regularized Low Rank Tensor Decomposition and Collaborative Representation

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