Semantic Change Detection of Carbon Sources and Sinks via Spatiotemporal Attention and Multiscale Fusion

High resolution remote sensing image semantic change detection (SCD) helps to accurately capture the spatial distribution and dynamic evolution of carbon sources and sinks by identifying changes in land cover types. However, existing methods suffer from the loss of spatial details and insufficient ability to model global features. Therefore, this letter proposes an SCD model based on spatiotemporal attention perception and multi-scale fusion (SC-SCDNet). The model introduces a multi-scale efficient cross attention block (MCA) in the encoder to bridge the semantic gap, and integrates a feature enhancement module (FEM) to enhance the semantic expression ability of small targets using multi-branch dilated convolution. In addition, a spatiotemporal channel window interaction module (TBCM) is designed to capture global information from both spatial and channel dimensions, enhancing spatial detail expression. The experimental results show that SC-SCDNet achieves the most advanced performance on SECOND and Landsat-SCD datasets, providing a better technical scheme for carbon sources and carbon sinks change detection.