Hyperspectral Anomaly Detection Using the Spectral–Spatial Graph-Reference-Cited by-同舟云学术

Hyperspectral Anomaly Detection Using the Spectral–Spatial Graph

Published:2022 Issue: Volume:60 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Tu Bing¹^ORCID,Wang Zhi¹^ORCID,Ouyang Huiting¹,Yang Xianchang¹^ORCID,Li Jun²^ORCID,Plaza Antonio³^ORCID

Affiliation:

1. School of Information Science and Technology, Hunan Institute of Science and Technology, Yueyang, China

2. Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan, China

3. Department of Technology of Computers and Communications, Escuela Politecnica, Hyperspectral Computing Laboratory, University of Extremadura, Cáceres, Spain

Funder

National Natural Science Foundation of China

Science Foundation for Distinguished Young Scholars of Hunan Province

Key Research and Development Program of Hunan Province

Foundation of Department of Water Resources of Hunan Province

Natural Science Foundation of Hunan Province

Foundation of Education Bureau of Hunan Province

Junta de Extremadura

Spanish Ministerio de Ciencia e Innovacion

European Union’s Horizon 2020 Research and Innovation Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09930793.pdf?arnumber=9930793

Reference47 articles.

1. Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection

2. Robust Graph Autoencoder for Hyperspectral Anomaly Detection

3. An introduction to spectral graph techniques for the analysis of hyperspectral image data

4. Visual Attention Based Hyperspectral Imagery Visualization

5. Multi-Scale Visual Attention Deep Convolutional Neural Network for Multi-Focus Image Fusion

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