SIAM-CDNET: A Remote Sensing Image Change Detection Network for Optimized Edge Detection and Mitigation of Pseudo Changes

Against the backdrop of urbanization and land use policy formulation, the accurate identification of changes is imperative for the effective monitoring and control of urban growth, conservation of natural resources, and environmental protection. Traditional remote sensing techniques for change detection, foundational to these applications, typically face significant challenges such as pronounced errors and inadequate parsing of complex environments. To surmount these obstacles, this study proposes a cutting-edge, deep learning-based strategy with a Siamese network framework, and utilizing a modified U-Net++ network (SIAM-CDNET) to improve change detection capabilities. This model integrates an attention mechanism to enhance the acuity and distinguishing features of remote sensing imagery, particularly for subtle variations and intricate structures in urban settings. Furthermore, an edge-detection component designed for multiscale feature amalgamation and a change detection-specific composite loss function are devised to increase the precision and generalizability of the model. Evaluations based on the LEVIR-CD,CDD and GZ-CD datasets indicate that the proposed SIAM-CDNET markedly surpasses existing methods in terms of accuracy and F1-score, with values of 90.50% and 90.82% (LEVIR-CD dataset),89.95% and 91.18% (CDD dataset) and 93.81% and 90.8%(GZ-CD dataset), respectively. These results confirm the exceptional performance of SIAM-CDNET in urban development and land use change detection, potentially making a significant technical contributions to the field.