Name
Title | ||
|---|---|---|
Disaster Area Detection from Synthetic Aperture Radar Images Using Convolutional Autoencoder and One-class SVM. |
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. (C) 2019 The Authors. Published by Atlantis Press SARL. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.2991/jrnal.k.190601.001 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | Field | DocType |
Anomaly detection,convolutional autoencoder,one-class SVM,synthetic aperture radar | Autoencoder,Pattern recognition,Synthetic aperture radar,Computer science,Support vector machine,Artificial intelligence,Disaster area | Journal |
Volume | Issue | ISSN |
6 | 1 | 2352-6386 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shingo Mabu | 1 | 493 | 77.00 |
| Kohki Fujita | 2 | 0 | 0.34 |
| Takashi Kuremoto | 3 | 196 | 27.73 |