Sar Image Change Detection via a Few-Shot Learning-Based Neural Network

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. Although convolutional neural network has been proposed for feature learning, it is necessary to collect numerous of samples to train a perfect model, which is difficult to achieve. In this paper, we propose a few-shot learning-based neural network to exploit the changed information from the noisy difference image. Being different from traditional training method with numerous labeled samples, in the proposed method, fewer samples are used to train a neural network. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.