Abstract
Accurate building change detection (BCD) in high-resolution (HR) remote sensing imagery faces persistent challenges including edge misdetection, omission errors, and coarse segmentation. We propose an effective feature-enhanced detection network (DVM-Net) for BCD in HR remote sensing images. This framework addresses these limitations through three key innovations: a dual-channel structure for comprehensive detail extraction, a multi-scale down-sampling module to preserve features and reduce errors, and a visual attention mechanism for refined edge detection. Evaluations include ablation studies on the Wuhan University Building Change Detection Dataset (WHU_CD) and cross-dataset validation using the Google Earth Image Change Detection Dataset (Google_CD) and Large-Scale Earth Vision Infrastructure Change Detection Dataset (LEVIR_CD). DVM-Net achieves state-of-the-art performance, surpassing 11 existing methods with mean Intersection over Union (mIoU) scores of 81.71 %, 79.77 %, and 86.53 % on these benchmarks, respectively. Analysis on the SEmantic Change detectiON Dataset (SECOND) identifies limitations in complex urban environments. The open-source implementation (https://github.com/AroundSeeYou/DVM_Net) provides an advanced solution for HR BCD tasks.