Self-Paced Probabilistic Collaborative Representation for Anomaly Detection of Hyperspectral Images-Reference-Cited by-同舟云学术

Self-Paced Probabilistic Collaborative Representation for Anomaly Detection of Hyperspectral Images

Published:2024 Issue: Volume:62 Page:1-10
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhang Chendi¹^ORCID,Su Hongjun²^ORCID,Wang Xiaolei¹^ORCID,Wu Zhaoyue³^ORCID,Yang Yufan¹^ORCID,Xue Zhaohui²^ORCID,Du Qian⁴^ORCID

Affiliation:

1. School of Earth Sciences and Engineering and Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment Using Remote Sensing, Hohai University, Nanjing, China

2. College of Geography and Remote Sensing, Hohai University, Nanjing, China

3. Department of Technology of Computer and Communication, University of Extremadura, Cáceres, Spain

4. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10508219.pdf?arnumber=10508219

Reference39 articles.

1. Nonlocal Low-Rank Tensor Completion for Visual Data

2. Maximum Likelihood Estimation-Based Joint Sparse Representation for the Classification of Hyperspectral Remote Sensing Images

3. A Novel Center-Boundary Metric Loss to Learn Discriminative Features for Hyperspectral Image Classification

4. Hyperspectral Image Classification Using Group-Aware Hierarchical Transformer

5. Multiscale and Cross-Level Attention Learning for Hyperspectral Image Classification

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection;Remote Sensing;2024-08-18