Multiple Spectral-Spatial Representation Based on Tensor Decomposition for HSI Anomaly Detection-Reference-Cited by-同舟云学术

Multiple Spectral-Spatial Representation Based on Tensor Decomposition for HSI Anomaly Detection

Published:2022 Issue: Volume:15 Page:3539-3551
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wang Yujian¹^ORCID,Li Dan¹^ORCID,Wu Hanjie¹,Li Xiaojun²,Kong Fanqiang¹^ORCID,Wang Qiang³^ORCID

Affiliation:

1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. National Key Laboratory Science and Technology Space Microwave, China Academic Space Technology, Xian, China

3. Control Science and Engineering, Harbin Institute of Technology, Harbin, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09766375.pdf?arnumber=9766375

Reference52 articles.

1. Total variation regularized weighted tensor ring decomposition for missing data recovery in remote sensing images;wang;IEEE Geosci Remote Sens Lett,2022

2. l₀-l₁ Hybrid Total Variation Regularization and its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing

3. Low-rank decomposition-based anomaly detection

4. Robust principal component analysis?

5. Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary

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