Semantic change detection using a hierarchical semantic graph interaction network from high-resolution remote sensing images

Current semantic change detection (SCD) methods face challenges in modeling temporal correlations (TCs) between bitemporal semantic features and difference features. These methods lead to inaccurate detection results, particularly for complex SCD scenarios. This paper presents a hierarchical semantic graph interaction network (HGINet) for SCD from high-resolution remote sensing images. This multitask neural network combines semantic segmentation and change detection tasks. For semantic segmentation, we construct a multilevel perceptual aggregation network with a pyramidal architecture. It extracts semantic features that discriminate between different categories at multiple levels. We model the correlations between bitemporal semantic features using a TC module that enhances the identification of unchanged areas. For change detection, we design a semantic difference interaction module based on a graph convolutional network. It measures the interactions among bitemporal semantic features, their corresponding difference features, and the combination of both. Extensive experiments on four datasets, namely SECOND, HRSCD, Fuzhou, and Xiamen, show that HGINet performs better in identifying changed areas and categories across various scenarios and regions than nine existing methods. Compared with the existing methods applied on the four datasets, it achieves the highest F1scd values of 59.48%, 64.12%, 64.45%, and 84.93%, and SeK values of 19.34%, 14.55%, 18.28%, and 51.12%, respectively. Moreover, HGINet mitigates the influence of fake changes caused by seasonal effects, producing results with well-delineated boundaries and shapes. Furthermore, HGINet trained on the Fuzhou dataset is successfully transferred to the Xiamen dataset, demonstrating its effectiveness and robustness in identifying changed areas and categories from high-resolution remote sensing images. The code of our paper is accessible at https://github.com/long123524/HGINet-torch.