Remote Sensing Image Change Detection Transformer Network Based on Dual-Feature Mixed Attention

Change detection (CD) of high-resolution remote sensing (RS) images is a basic task in RS image processing tasks. In recent years, CD tasks have made many attempts in pure convolutional networks, attention mechanism, and transformer, and have achieved good results. Based on the power of attention and transformers, we hope to find a method that can handle the details of the image better and has better generalization ability. In this article, we propose a dual-feature mixed attention-based transformer network (DMATNet). First, we adopt a dual-feature extraction method, using a simple convolutional neural network (CNN) to extract coarse features, and a CNN based on progressive sampling to extract fine features. Then, we fuse the fine and coarse features with dual-feature mixed attention (DFMA) module. It can not only extract more specific regions of interest, but also overcome the misjudgment caused by oversampling, and synchronize feature extraction and target information integration. Finally, we use transformer to optimize these extracted information and feedback into the original features in the encoder to help remodel the pixel space. We merged the DMAT network into a deep feature difference-based CD framework and conducted extensive experiments on four datasets, LEVIR-CD, DSIFN-CD, WHU-CD, and CLCD, respectively, with tested F1 and interconnection over union (IoU) results of 90.75%/84.13%, 71.23%/55.32%, 85.70%/74.98%, and 66.56%/59.87%. Experimental results show that our DMAT-based model performs significantly better than the existing state-of-the-art attention and transformer-based methods.