A Deep Generalized Correlation Network for Bitemporal Image Change Detection

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most methods are developed based on the traditional framework that exploits the changed region from a difference image (DI) that is usually subject to the speckle. To essentially solve this issue, in this paper, we propose a deep canonic correlation network for bitemporal SAR image. In the proposed network, bitemporal SAR images and its corresponding DI are taken as the inputs and then three deep neural networks are designed to employ their features, respectively. Then the changed regions are obtained by the exploited features. Finally, we compare the proposed method with other deep learning methods and perform the comparison on four sets of bitemporal SAR images. The experimental results show that our proposed method outperforms other methods.