Hyperspectral Anomaly Detection With Relaxed Collaborative Representation
Author:
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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