Semantic-Aware Alignment Network for Cross-Resolution Change Detection

Cross-resolution change detection (CRCD) is of significant practical importance in disaster assessment, rapid urban transitions, and various applications. Conventional change detection methods are primarily tailored for bitemporal images with consistent spatial resolution, rendering them unsuitable for direct application to CRCD tasks. This limitation stems from the substantial scale differences and pixel-wise misalignment prevalent in cross-resolution remote sensing images. In response to these challenges, we introduce a semantic-aware alignment network (SA-Net). SA-Net utilizes cross-attention to map bitemporal images into a shared semantic space, effectively alleviating the difficulties of the subsequent alignment arising from semantic mismatches. Furthermore, a joint transformer featuring an encoder-decoder architecture is employed to extract global information and learn the geometric parameters for spatial alignment between bitemporal images. Experimental evaluations on two real-collected datasets, HTCD and MRCDD, showcase the superior performance of our proposed SA-Net in CRCD tasks.