Multiscale Fusion CNN-Transformer Network for High-Resolution Remote Sensing Image Change Detection
Ming Jiang, Yimin Chen, Zhe Dong, Xiaoping Liu, Xinchang Zhang, Honghui Zhang
Abstract
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.