Estfnet: an efficient spatiotemporal fusion network for detection in Remote Sensing imagery

Semantic change detection (SCD) is a critical task in the field of remote sensing, which aims to provide semantic understanding of remote sensing imagery by simultaneously detecting change regions and classifying their semantic categories. It plays a significant role in applications such as land use monitoring and ecological environment assessment. With the increasing availability of bi-temporal remote sensing datasets, a variety of deep learning-based models have been widely applied to the task of SCD detection. However, existing methods still suffer from limitations in modelling the interaction of cross-temporal semantic features and in the adaptive fusion of differential information, which constrains detection accuracy. Thus, to address these limitations, we propose ESTFNet, an efficient spatiotemporal fusion network for SCD detection. The network is built around two core modules: the Efficient Spatiotemporal Cross-Attention (ESTCA) and the (BDAF), which address feature interaction and feature fusion, respectively. Extensive experimental results demonstrate that ESTFNet outperforms several recent representative SCD models on remote sensing datasets, showing superior performance in complex scenarios. The code of our ESTFNet is available at https://github.com/loudongang/ESTFNet.