Remote Sensing Image Change Detection of Buildings Based on Improved Swin-Transformer

Traditional change detection methods suffer from limited precision and inadequate feature discriminability in images. Although deep learning has considerably advanced the building change detection field, challenges persist due to the diverse change types in remote sensing imagery across distinct epochs, leading to complex backdrops and arduous feature extraction. To address this issue, we propose a novel approach that combines convolutional neural networks (CNNs) with Swin Transformers to further enhance the accuracy of building change detection in remote sensing images. Firstly, CNNs are employed for preliminary feature extraction from input images. Subsequently, the Transformer's self-attention mechanism is utilized to obtain more comprehensive semantic information about the building structures within the feature maps. Lastly, leveraging the multi-level feature extraction advantages of Swin Transformers, we construct a network capable of fusing and extracting building change features in remote sensing images. By introducing a differential feature fusion module during the decoding phase, the model's receptive field in multi-scale feature fusion processes is enhanced, along with the reinforcement of local details and improvement of segmentation precision. Experimental comparisons on the WUH-CD dataset reveal that, compared to the classic change detection network FC-EF, our proposed method has yielded improvements of 1.7%, 23.7%, 18.7%, 13.5%, and 27.5% in overall accuracy, precision, F1, recall rate, and IoU, respectively.