Attention Filtering Network Based on Branch Transformer for Change Detection in Remote Sensing Images

The emergence of high-resolution (HR) remote sensing imagery showcases the continual advancements in remote sensing technology but also sets higher demands for related tasks in the field, including remote sensing image change detection. Due to their outstanding performance in extracting salient features, convolutional neural networks (CNNs) have played a significant role and become widely utilized in many computer vision tasks. The encoder-decoder structure has confirmed the effectiveness of integrating multilevel feature information, as it allows for the synthesis of both local and global information of features. The exploration of the potential relationships between multilevel features and their efficient integration remains of significant importance. Furthermore, thanks to the advent of the transformer, many modern approaches have seen great improvements in high-level semantic understanding of images. In this article, we propose an attention-filtering network based on a branch transformer for effective change detection in remote sensing images. A hybrid attention fusion module (HAFM) is used to efficiently fuse features of different granularities and perform progressive information filtering on the extracted multilevel features to obtain an effective change feature. We also propose a branch transformer block (BTB) to efficiently aggregate global long-range dependencies and spatial details from the change feature. Extensive comparative experiments conducted on three different HR remote sensing datasets have verified the effectiveness of our method.