Dynamic Low-Rank and Sparse Priors Constrained Deep Autoencoders for Hyperspectral Anomaly Detection
Author:
Affiliation:
1. School of Aerospace Science and Technology, Xidian University, Xi’an, China
2. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
Funder
National Natural Science Foundation of China
Innovation Fund of Xidian University
Innovation Capability Support Program of Shaanxi
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10367905/10285414.pdf?arnumber=10285414
Reference67 articles.
1. Global Consistent Graph Convolutional Network for Hyperspectral Image Classification
2. Multiview-Based Random Rotation Ensemble Pruning for Hyperspectral Image Classification
3. A Divisive Hierarchical Clustering Approach to Hyperspectral Band Selection
4. Spectral Difference Guided Graph Attention Autoencoder for Hyperspectral Anomaly Detection
5. Hyperspectral Anomaly Detection Using Quantum Potential Clustering
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