Full-Scale Change Detection Network for Remote Sensing Images Based on Deep Feature Fusion

In the processing of high-resolution remote sensing images, multiscale feature fusion techniques are commonly employed to construct change detection models, aiming to capture the details and characteristics of target objects at different scales. However, current change detection methods often neglect the subtle features of small-scale targets and edge information during feature fusion. To address this issue, this article proposes a deep feature fusion network (DFFNet) for full-scale change detection in remote sensing images. DFFNet enhances the accuracy of boundary information in change regions and enables the extraction of full-scale change features. By integrating phased difference extraction techniques with an enhanced attention mechanism, a dual temporal difference enhancement module (DT-DEM) is designed to comprehensively extract change detection information across scales. In addition, combining an inverted pyramid technique with an attention mechanism, a screening function inverted pyramid network (S-FIPN) is proposed, which efficiently extracts and fuses full-scale features while significantly improving the extraction of subtle changes and weak edge features. Comparison experiments with ten state-of-the-art (SOTA) algorithms on four publicly available datasets, namely, LEVIR-CD, WHU-CD, NJDS, and MSRS-CD, show that DFFNet achieves the best $F1$ values on all datasets, which are 92.50%, 90.55%, 81.04%, and 77.44%, respectively. These results underscore the superiority and effectiveness of DFFNet in full-scale feature extraction and edge information retrieval.