Volume 6, Issue 1, June 2019, Pages 48 - 51
Disaster Area Detection from Synthetic Aperture Radar Images Using Convolutional Autoencoder and One-class SVM
Shingo Mabu*, Kohki Fujita, Takashi Kuremoto
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi 755-8611, Japan
*Corresponding author. Email: firstname.lastname@example.org
Received 15 November 2017, Accepted 18 December 2017, Available Online 25 June 2019.
- 10.2991/jrnal.k.190601.001How to use a DOI?
- Anomaly detection; convolutional autoencoder; one-class SVM; synthetic aperture radar
In recent years, research on detecting disaster areas from synthetic aperture radar (SAR) images has been conducted. When machine learning is used for disaster area detection, a large number of training data are required; however, we cannot obtain so much training data with correct class labels. Therefore, in this research, we propose an anomaly detection system that finds abnormal areas that deviate from normal ones. The proposed method uses a convolutional autoencoder (CAE) for feature extraction and one-class support vector machine (OCSVM) for anomaly detection.
- © 2019 The Authors. Published by Atlantis Press SARL.
- 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 - Kohki Fujita AU - Takashi Kuremoto PY - 2019 DA - 2019/06/25 TI - Disaster Area Detection from Synthetic Aperture Radar Images Using Convolutional Autoencoder and One-class SVM JO - Journal of Robotics, Networking and Artificial Life SP - 48 EP - 51 VL - 6 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.190601.001 DO - 10.2991/jrnal.k.190601.001 ID - Mabu2019 ER -