Name
Title | ||
|---|---|---|
Anomaly Detection Using Convolutional Adversarial Autoencoder And One-Class Svm For Landslide Area Detection From Synthetic Aperture Radar Images |
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. (C) 2021 The Authors. Published by Atlantis Press International B.V. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.2991/jrnal.k.210713.014 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | DocType | Volume |
Anomaly detection, adversarial autoencoder, one-class SVM, synthetic aperture radar | Journal | 8 |
Issue | ISSN | Citations |
2 | 2352-6386 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shingo Mabu | 1 | 493 | 77.00 |
| Soichiro Hirata | 2 | 0 | 0.34 |
| Takashi Kuremoto | 3 | 196 | 27.73 |