Geometric Variation Adaptive Network for Remote Sensing Image Change Detection

Change detection identifies surface changes on the Earth by comparing two images from the same area at different times. To generate smooth change maps, a common method is fusing information from neighboring areas around each pixel. While the conventional fusion methods primarily rely on fixed regular-shaped neighboring areas, which may be inadequate in capturing the diverse and irregular geometric structures of changed ground objects. To address this limitation, we propose a novel geometric variation adaptive change detector (GVA-CD), which adaptively adjusts the shape and size of neighboring areas based on the geometrical structure of ground objects. More specifically, we design a new geometric variation adaptive module (GVAM) as a component of GVA-CD. GVAM captures the structure of the ground objects to construct geometrically flexible neighboring areas for each pixel, enabling the model to adapt to different ground object structures and generate discriminative difference features. We further propose a new difference measurement module to compute the difference between the features of pre- and post-change images by leveraging the adaptive neighboring areas. In addition, the GVA-CD introduces a multistage cross-scale fusion mechanism in both feature extraction and change map generation, to enhance the scale adaption ability of the feature extraction and change map generation. Extensive experiments on three large datasets demonstrate that our GVA-CD can outperform existing methods in change detection.