A Temporal Correlation Networks Based on Interactive Modelling for Remote Sensing Images Change Detection

Change detection identifies dynamic changes in surface cover and feature status by comparing remote sensing images at different points in time, which is of wide application value in the fields of disaster early warning, urban management and ecological monitoring. Mainstream datasets are dominated by long‐term datasets; to support short‐term change detection, we collected a new dataset, HNU‐CD, which contains some small and hard‐to‐identify change regions. A time correlation network (TCNet) is also proposed to address these challenges. First, foreground information is enhanced by interactively modelling foreground relations, while background noise is smoothed. Secondly, the temporal correlation between bit‐time images is utilised to refine the feature representation and minimise false alarms due to irrelevant changes. Finally, a U‐Net inspired architecture is adapted for dense upsampling to preserve details. TCNet demonstrates excellent performance on both the HNU‐CD (Hainan University change detection dataset) dataset and three widely used public datasets, indicating that its generalisation capabilities have been enhanced. The ablation experiments provide a good demonstration of the ability to reduce the impact caused by pseudo‐variation through temporal correlation modelling.