Tensor Approximation With Low-Rank Representation and Kurtosis Correlation Constraint for Hyperspectral Anomaly Detection
Author:
Affiliation:
1. Department of Information Engineering, School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
Funder
National Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09828395.pdf?arnumber=9828395
Reference56 articles.
1. Hyperspectral Anomaly Detection Using Dual Window Density
2. Hyperspectral Anomaly Detection Based on Machine Learning: An Overview
3. Anomaly detection using morphology-based collaborative representation in hyperspectral imagery
4. Collaborative Representation for Hyperspectral Anomaly Detection
5. Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation
Cited by 2 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. Matrix Factorization With Framelet and Saliency Priors for Hyperspectral Anomaly Detection;IEEE Transactions on Geoscience and Remote Sensing;2023
2. Robust Tensor Low-Rank Sparse Representation With Saliency Prior for Hyperspectral Anomaly Detection;IEEE Transactions on Geoscience and Remote Sensing;2023
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