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

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 pseudochanges and omissions. To address this issue, we propose a multiscale 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 multiscale features using a CNN-transformer hybrid structure to extract global and local features. In the decode stage, a gated attention module is used to filter the extracted features layer by layer. Moreover, before outputting the prediction results, a feature refinement head 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.

Keywords

• Attention mechanism
• change detection
• high-resolution remote sensing image
• multilevel aggregation
• transformer