Hyperspectral Anomaly Detection Based on Graph Regularized Variational Autoencoder-Reference-Cited by-同舟云学术

Hyperspectral Anomaly Detection Based on Graph Regularized Variational Autoencoder

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Wei Jie¹,Zhang Jingfa¹,Xu Yang²^ORCID,Xu Lidan¹,Wu Zebin¹^ORCID,Wei Zhihui¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

2. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Funder

National Natural Science Foundation of China

Jiangsu Provincial Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Key Projects of University Natural Science Fund of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09855514.pdf?arnumber=9855514

Reference23 articles.

1. A stacked autoencoders-based adaptive subspace model for hyperspectral anomaly detection

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

3. Unsupervised Pixel-Wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks

4. Hyperspectral anomaly detection based on autoencoder and spatial morphology extraction

5. Hyperspectral Anomaly Detection With Robust Graph Autoencoders

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