SAR Image Change Detection Method via a Pyramid Pooling Convolutional 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. In this paper, we propose a novel mutli-scale average pooling (MSAP) network to exploit the changed information from the noisy difference image. Being different from traditional convolutional network with only an one-scale pooling kernel, in the proposed method, multi -scale pooling kernels are equipped in convolutional network to obtain the spatial context information on changed regions from the difference image. 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.