Hyperspectral Anomaly Detection Based on Machine Learning: An Overview-Reference-Cited by-同舟云学术

Hyperspectral Anomaly Detection Based on Machine Learning: An Overview

Published:2022 Issue: Volume:15 Page:3351-3364
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:

Xu Yichu¹,Zhang Lefei¹^ORCID,Du Bo²,Zhang Liangpei³^ORCID

Affiliation:

1. Hubei Luojia Laboratory, Wuhan, China

2. National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence, Wuhan University, Wuhan, China

3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

Special Fund of Hubei Luojia Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09760098.pdf?arnumber=9760098

Reference90 articles.

1. Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder

2. Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection

3. Spectral–Spatial Anomaly Detection via Collaborative Representation Constraint Stacked Autoencoders for Hyperspectral Images

4. Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection

5. Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

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