Hyperspectral Anomaly Detection With Guided Autoencoder
Author:
Affiliation:
1. School of Physics and Optoelectronic Engineering, Xidian University, Xi’an, China
2. School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
Funder
Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, South Korea
BK21 FOUR Project
Overseas Expertise Introduction Project for Discipline Innovation
China Scholarship Council for Joint Ph.D. Students
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09893839.pdf?arnumber=9893839
Reference51 articles.
1. Denoising Adversarial Autoencoders
2. Exploiting Embedding Manifold of Autoencoders for Hyperspectral Anomaly Detection
3. Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection
4. Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery
5. Low-Rank and Sparse Decomposition With Mixture of Gaussian for Hyperspectral Anomaly Detection
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