LAUSR

Deep-learning-based change detection methods have achieved remarkable success through the feature learning capability of deep convolutions. However, the network structures of existing methods are simply modified from the semantic segmentation models, ignoring the essential characteristics of change detection, thereby limiting their applications. In this work, we propose a category-awareness-based difference-threshold alternative-learning network (D-TNet) for remote sensing image change detection. Our motivation is to characterize the different change magnitudes for different land cover changes, and represent the semantic content differences of various objects. Thus, our D-TNet consists of a difference map (DM) learning path and a threshold map (TM) learning path, realizing self-adapting threshold selection by assigning each pixel a unique threshold. The two paths are alternatively optimized to make the DM more discriminative, as well as making the TM more adaptive. In addition, a category-awareness attention mechanism is introduced in D-TNet, which learns a pixel-to-category relationship to benefit in representing the heterogeneity of land covers. Finally, experimental results on three change detection datasets verify the effectiveness of our D-TNet in both visual and quantitative analyses. Code will be available at: https://www.researchgate.net/profile/Ling-Wan-4.