Multiscale Alignment and Progressive Feature Fusion Network for High Resolution Remote Sensing Images Change Detection

The advancement of deep learning technology has significantly improved the performance of high-resolution remote sensing (HRRS) image change detection (CD) task. HRRS images on CD are usually captured under varying conditions, which may generate pseudo-changes caused by seasonal variations, changes in lighting angles, and object motion. These factors can interfere with the effective modeling of difference information. However, conventional methods of modeling difference information may not adequately address these challenges, leaving them susceptible to such factors. Therefore, this article proposes a multiscale alignment and progressive feature fusion network (MAPNet) based on convolutional neural network (CNN) and transformer, which can effectively model difference information. First, a flow- and attention-guided bitemporal alignment module (FA-BAFM) is designed to align bitemporal features and capture the differences between them. Second, a progressive difference feature fusion module (PDFFM) is developed to comprehensively fuse the difference information across various scales and levels. Finally, a local feature enhancement module (LFEM) is constructed to improve the ability of the backbone to extract both global and local features. Experimental results on the learning, vision, and remote sensing CD (LEVIR-CD), Wuhan University (WHU), and Sun Yat-Sen University CD (SYSU-CD) datasets show that MAPNet achieves F1-scores of 91.90%, 94.19%, and 83.69%, respectively, outperforming the state-of-the-art (SOTA) methods. The demo code will be released at https://github.com/zlin9/mapnet.