Multidomain Constrained Translation Network for Change Detection in Heterogeneous Remote Sensing Images

In heterogeneous image change detection (HICD), preventing neural networks from distorting critical information is the main challenge of such methods based on deep translation. Most of these methods rely on a priori information to suppress the effects of changed pixels in the translation process, but the accuracy of the prior information will influence the results of translation. In this article, we propose an end-to-end multidomain constrained translation network (MDCTNet) for unsupervised HICD. The proposed MDCTNet utilizes an improved generative adversarial network (GAN) to generate target domain images realistically. Furthermore, to retain the content information of the source domain images, MDCTNet leverages contrastive learning to ensure the consistency of adjacent pixel relationships. Meanwhile, it employs high-frequency information consistency which preserves pivotal characteristics. We compare the proposed MDCTNet with state-of-the-art algorithms to verify the efficacy of the proposed technique. The experimental results on five real datasets demonstrate the effectiveness of the proposed method.