MDS-Net: An Image-Text Enhanced Multimodal Dual-Branch Siamese Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD), which aims to identify differences between bitemporal images, has made great progress through the application of deep learning methods. Convolutional neural networks and transformers are extensively employed in remote sensing image change detection, achieving promising results. However, current models predominantly focus on visual representation learning while neglecting the potential of multimodal learning methods. Consequently, this leads to issues such as inaccurate identification of nonsemantic changes, incomplete boundary extraction due to the degradation of local feature details, and the loss of small targets. Recently, a novel method for efficiently learning from natural language supervision, known as contrastive language image pretraining, has been proposed. Inspired by this work, we aim to leverage pixel-text relationships to guide the training of detection (RSCD) models. We propose a novel end-to-end multimodal dual-branch Siamese network, named MDS-Net, specifically designed for RSCD. In addition, we propose a U-Shaped vision-language guided transformer multimodal decoder to enhance the interactions between visual and textual information. To capture detailed semantic change features, we also introduce a novel difference feature enhancement. module. Extensive experiments demonstrate that our model outperforms other state-of-the-art methods, with IoU values of 85.60%, 76.81%, 89.55%, 94.51%, and 71.77% on the LEVIR-CD, LEVIR-CD+, WHUCD, CDD, and SYSU-CD datasets, respectively.