Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective-Reference-Cited by-同舟云学术

Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective

Published:2024 Issue: Volume:17 Page:13966-13980
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 Minghua¹^ORCID,Gao Lianru²^ORCID,Ren Longfei²^ORCID,Sun Xian³^ORCID,Chanussot Jocelyn⁴^ORCID

Affiliation:

1. Institute of Robotics and Automatic Information System (IRAIS), College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics (tjKLIR), Nankai University, Tianjin, China

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

3. Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

4. CNRS, Grenoble INP, GIPSA-Lab, Université Grenoble Alpes, Grenoble, France

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

MIAI@Grenoble Alpes

AXA Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10621578.pdf?arnumber=10621578

Reference65 articles.

1. X-Shaped Interactive Autoencoders With Cross-Modality Mutual Learning for Unsupervised Hyperspectral Image Super-Resolution

2. Model-Informed Multistage Unsupervised Network for Hyperspectral Image Super-Resolution

3. Vision Transformer With Contrastive Learning for Remote Sensing Image Scene Classification

4. Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach

5. HADGSM: A Unified Nonconvex Framework for Hyperspectral Anomaly Detection