基于自校准的增强差异引导遥感影像变化检测方法OA

The high-precision change detection of remote sensing images is of great value in fields such as geographic analysis,urban monitoring,and land use assessment.In recent years,change detection networks based on convolutional neural networks and vision transformers have made significant progress,and have demonstrated outstanding performance in fusing dual-temporal image features.However,existing networks have deficiencies in geometric modeling and edge representation,which often results in incomplete boundary details and thus inaccurate positioning of change regions.To address these limitations,in this paper,an enhanced difference-guided change detection network based on self-calibration(SEDGNet)is proposed.First,an adaptive square calibration module(ASCM)is constructed.The global context along the horizontal and vertical axes is modeled to explicitly capture the structural patterns in change regions.While enhancing geometric awareness,it combines a multi-scale fusion module to effectively integrate the differential information from dual-temporal images.Second,a differential fusion guidance module(DFGM)is designed,which integrates encoder features,decoder outputs,and high-frequency differential features to strengthen the edge representation in change areas.Finally,tests are conducted on three public datasets to validate the proposed network.The results show that the proposed network outperformed existing advanced networks across multiple evaluation metrics,verifying its effectiveness and superiority in high-precision change detection tasks.