Multiscale Fusion CNN-Transformer Network for High-Resolution Remote Sensing Image Change Detection

Accurate change detection using remote sensing data is crucial for understanding surface dynamics. Despite the impressive success of current convolutional neural network (CNN)-based techniques, their feature extraction and representation capabilities are limited, leading to pseudo-changes and omissions. To address this issue, we propose a multi-scale fusion CNN-Transformer network (MSFCTNet), which incorporates the strengths of CNN and Transformer to improve change detection capability. The network utilizes a Siamese CNN to extract features from the bi-temporal image pairs and then combines multi-scale features using a CNN-Transformer hybrid structure (HCTM) to extract global and local features. In the decode stage, a gated attention module (GAM) is used to filter the extracted features layer by layer. Moreover, before outputting the prediction results, a feature refinement head (FRH) is employed to further refine the features, suppress background noise, and improve detection capability. LEVIR-CD, SVCD, and SYSU-CD datasets are used to evaluate the proposed network. Experimental results indicate that MSFCTNet outperforms other state-of-the-art change detection methods, proving the potential of MSFCTNet for change detection tasks.