HETEROGENEOUS IMAGE CHANGE DETECTION BASED ON DEEP IMAGE TRANSLATION AND FEATURE REFINEMENT-AGGREGATION

Remote sensing change detection (CD) has been widely studied, and the CD of heterogeneous images based on cross-sensor acquisition has significant research significance. However, the scarcity of heterogeneous data and the difficulty in obtaining high-quality change maps remain significant challenges. To address these issues, we propose a deep image translation-based feature refinement-aggregation change detection network (FRAN) designed for heterogeneous images, such as optics and SAR images. First, we use data augmentation to increase the number of available images and a no-independent-component-for-encoding GAN (NICE-GAN) to translate the features from the optical domain to the SAR image domain, enabling direct comparison of images from different domains. Finally, we introduce feature refinement module and feature aggregation module to extract more accurate change information and obtain an accurate change region. Our experiments on two public datasets demonstrate that the proposed FRAN’s re-detection accuracy is superior to that of four other heterogeneous detection methods.