Anomaly Detection Using Convolutional Adversarial Autoencoder and One-class SVM for Landslide Area Detection from Synthetic Aperture Radar Images

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...

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Veröffentlicht in:Journal of robotics, networking and artificial life Jg. 8; H. 2; S. 139 - 144
Hauptverfasser: Mabu, Shingo, Hirata, Soichiro, Kuremoto, Takashi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 2021
Schlagworte:
Research Article
synthetic aperture radar
adversarial autoencoder
Anomaly detection
one-class SVM
ISSN:2405-9021, 2352-6386
Online-Zugang:Volltext
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Zusammenfassung: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.
ISSN:2405-9021
2352-6386
DOI:10.2991/jrnal.k.210713.014