A Coarse-to-Fine Hyperspectral Target Detection Method Based on Low-Rank Tensor Decomposition-Reference-Cited by-同舟云学术

A Coarse-to-Fine Hyperspectral Target Detection Method Based on Low-Rank Tensor Decomposition

Published:2023 Issue: Volume:61 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Feng Shou¹^ORCID,Feng Rui¹,Wu Dan¹,Zhao Chunhui¹,Li Wei²^ORCID,Tao Ran²^ORCID

Affiliation:

1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

2. 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

Open Fund of State Key Laboratory of Remote Sensing Science

Heilongjiang Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10305580.pdf?arnumber=10305580

Reference66 articles.

1. Spectral–Spatial Anomaly Detection via Collaborative Representation Constraint Stacked Autoencoders for Hyperspectral Images

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

3. Efficient coarse-to-fine spectral rectification for hyperspectral image

4. Antinoise Hyperspectral Image Fusion by Mining Tensor Low-Multilinear-Rank and Variational Properties

5. Combined sparse and collaborative representation for hyperspectral target detection

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