Fusion-Former: Fusion Features across Transformer and Convolution for Building Change Detection

Abstract

Change detection (CD) in remote sensing images is a technique for analyzing and characterizing surface changes from remotely sensed data from different time periods. However, due to the diverse nature of targets in complex remote sensing scenarios, the current deep-learning-based methods still sometimes suffer from the problem of the extracted features not being discriminative enough, resulting in false detections and detail loss. To solve these challenges, we propose a method called Fusion-Former for building change detection. Our approach fuses window-based self-attention with depth-wise convolution, which is named Fusion-Block and which combines convolutional neural networks (CNN) and a transformer to integrate information at different scales effectively. Moreover, in order to significantly enhance the performance of the transformer and the effect of Fusion-Block, an innovative attention module called Vision-Module is introduced. On the LEVIR-CD dataset and WHU-CD dataset, our model achieved F1-scores of 89.53% and 86.00%, respectively, showcasing its superior performance over state-of-the-art methods.