Authors
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: [email protected]
Corresponding Author
Shingo Mabu
Received 15 November 2017, Accepted 18 December 2017, Available Online
25 June 2019.
DOI
https://doi.org/10.2991/jrnal.k.190601.001
Keywords
Anomaly detection; convolutional autoencoder; one-class SVM; synthetic
aperture radar
Abstract
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.
Copyright
© 2019 The Authors. Published by ALife Robotics Corp. Ltd.
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/).