EDGE-GUIDED ENHANCEMENT NETWORK FOR BUILDING CHANGE DETECTION OF REMOTE SENSING IMAGES WITH A HYBRID CNN-TRANSFORMER ARCHITECTURE

The utilization of remote sensing images for building change detection has become a focal point of concern. Many contemporary change detection methodologies primarily focus on extracting more discriminative features while neglecting the use of prior edge information, leading to inaccurate detection results, especially in areas like building boundaries. To address this issue, we propose a method called Edge-Guided Enhancement Network(EGENet) that combines discriminative information and edge features within a unified framework. In particular, as edge represents part of the image that changes drastically and indicates high-frequency information in image, we construct an Edge Enhancement Module (EEM) to enhance high-frequency information and suppress noise. Furthermore, We design the Edge Detection Branch (EDB) to better enhance edge information and employ the detected edge information for subsequent cross attention. Our experiments on two building change detection datasets demonstrate that our approach is proper and achieves advanced performance compared to other methods.