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Ship Detection in SAR Images Using Convolutional Variational Autoencoders

We propose an unsupervised framework for ship detection in SAR image data, based on anomaly detection. We first learn representations of the SAR images with a convolutional Variational Autoencoder. Aftwerwards, we perform anomaly detection based on those representations, with a clustering algorithm. Experimental results with real SAR data are provided to illustrate the performance of the proposed algorithm.