Landslide area detection from synthetic aperture radar images using convolutional adversarial autoencoder and one-class SVM

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 and one-class SVM. In the experiments, the ability in detecting normal and abnormal areas is evaluated.