Volume 8, Issue 2, September 2021, Pages 139 - 144
Anomaly Detection Using Convolutional Adversarial Autoencoder and One-class SVM for Landslide Area Detection from Synthetic Aperture Radar Images
Authors
Shingo Mabu1, *, Soichiro Hirata1, Takashi Kuremoto2
1Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi 755-8611, Japan
2Department of Information Technology and Media Design, Nippon Institute of Technology, 4-1 Gakuendai, Miyashiro-machi, Minamisaitama-gun, Saitama 345-8501, Japan
*Correponding author. Email: mabu@yamaguchi-u.ac.jp
Corresponding Author
Shingo Mabu
Received 25 November 2020, Accepted 23 May 2021, Available Online 24 July 2021.
- DOI
- 10.2991/jrnal.k.210713.014How to use a DOI?
- Keywords
- Anomaly detection; adversarial autoencoder; one-class SVM; synthetic aperture radar
- Abstract
An anomaly detection model using deep learning for detecting disaster-stricken (landslide) areas in synthetic aperture radar images is proposed. Since it is difficult to obtain a large number of training images, especially disaster area images, with annotations, we design an anomaly detection model that only uses normal area images for the training, where the proposed model combines a convolutional adversarial autoencoder, principal component analysis, and one-class support vector machine. In the experiments, the ability in detecting normal and abnormal areas is evaluated.
- Copyright
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Shingo Mabu AU - Soichiro Hirata AU - Takashi Kuremoto PY - 2021 DA - 2021/07/24 TI - Anomaly Detection Using Convolutional Adversarial Autoencoder and One-class SVM for Landslide Area Detection from Synthetic Aperture Radar Images JO - Journal of Robotics, Networking and Artificial Life SP - 139 EP - 144 VL - 8 IS - 2 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.210713.014 DO - 10.2991/jrnal.k.210713.014 ID - Mabu2021 ER -