BS<sup>3</sup>LNet: A New Blind-Spot Self-Supervised Learning Network for Hyperspectral Anomaly Detection-Reference-Cited by-同舟云学术

BS³LNet: A New Blind-Spot Self-Supervised Learning Network for Hyperspectral Anomaly Detection

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Gao Lianru¹^ORCID,Wang Degang¹^ORCID,Zhuang Lina¹^ORCID,Sun Xu¹^ORCID,Huang Min¹,Plaza Antonio²^ORCID

Affiliation:

1. Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

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

Funder

National Natural Science Foundation of China

Spanish Ministerio de Ciencia e Innovación

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10049187.pdf?arnumber=10049187

Reference58 articles.

1. Multimodal Hyperspectral Unmixing: Insights From Attention Networks

2. Joint Reconstruction and Anomaly Detection From Compressive Hyperspectral Images Using Mahalanobis Distance-Regularized Tensor RPCA

3. Hyperspectral Image Restoration with Self-supervised Learning: A Two-stage Training Approach

4. Adaptive Hyperspectral Mixed Noise Removal

5. Noise2Void - Learning Denoising From Single Noisy Images

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