GLAI-Net: Global-Local Awareness Integrated Network for Semantic Change Detection in Remote Sensing Images

Semantic change detection (SCD) in remote sensing images can simultaneously obtain changed areas and the transformation of ground objects, providing finer-grained information support in ground observation applications. Recently, multi-task networks have become the main paradigm for SCD, but their detection performance is still affected by limited multi-scale feature extraction and insufficient cross-task information interaction. To address these issues, we propose a global-local awareness integrated network (GLAI-Net) for SCD in remote sensing images. We design a parallel encoding structure and utilize convolutional neural networks and Transformer to achieve multi-scale modeling of images and enhance feature expression ability. Meanwhile, we propose multi-scale feature fusion (MSFF) modules in GLAI-Net to enhance the focus of detail features on changed objects with different sizes. Between the classification and change detection decoding branches, we propose semantic change response (SCR) modules in GLAI-Net that fully utilize the correlation between different tasks to improve the consistency and accuracy of detection results. Qualitative and quantitative results on the Landsat-SCD and SECOND datasets indicate that the proposed GLAI-Net outperforms the comparison methods in SCD performance, with SeKs of 0.5182 and 0.2063, respectively. In addition, the ablation experiment results confirm the effectiveness of MSFF and SCR modules in improving feature diversity and optimizing SCD performance.