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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Vydané v:	Journal of robotics, networking and artificial life Ročník 8; číslo 2; s. 139 - 144
Hlavní autori:	Mabu, Shingo, Hirata, Soichiro, Kuremoto, Takashi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 2021
Predmet:	Research Article synthetic aperture radar adversarial autoencoder Anomaly detection one-class SVM
ISSN:	2405-9021, 2352-6386
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