Hyperspectral Anomaly Detection With Total Variation Regularized Low Rank Tensor Decomposition and Collaborative Representation-Reference-Cited by-同舟云学术

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

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Feng Shou¹^ORCID,Wu Dan¹,Feng Rui¹,Zhao Chunhui¹

Affiliation:

1. College of Information and Communication Engineering and the Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China

Funder

National Natural Science Foundation of China

Heilongjiang Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09762719.pdf?arnumber=9762719

Reference18 articles.

1. Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery

2. Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

3. Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

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

5. Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition

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