GS4-4 Anomaly Detection of Disaster Areas from Satellite Images Using Convolutional Autoencoder and One-class SVM

Title:	GS4-4 Anomaly Detection of Disaster Areas from Satellite Images Using Convolutional Autoencoder and One-class SVM
Publication:	ICAROB2018
Volume:	23
Pages:	116-119
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2018.GS4-4
Author(s):	Kohki Fujita, Shingo Mabu, Takashi Kuremoto
Publication Date:	February 2, 2018
Keywords:
Abstract:	In recent years, research on detecting disaster areas from satellite 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) and One-class SVM (OCSVM).
PDF File:	https://alife-robotics.co.jp/members2018/icarob/data/html/data/GS_pdf/GS4/GS4-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/