A remote sensing image change detection method based on prior change information

Change detection in remote sensing is an important technique used to monitor surface changes on Earth. However, due to the complexity of remote sensing images and noise interference, traditional change detection methods and some deep learning methods often suffer from incomplete change boundaries and missed or false detections. To address these issues, we propose a Prior Change Information Guided Network (PCIGNet). PC IGNet utilizes known change information as prior knowledge to guide the network in accurately predicting change regions during the detection process. First, we introduce a self-attention module called the Guided Fusion Module (GFM), which overcomes the limitations of traditional convolutional fixed receptive fields and effectively captures long -range information. It leverages the high-level semantic representations of deep features and the local detailed information of shallow features as prior knowledge to aid in the progressive concatenation and fusion of multi -level features. In addition, we propose a Feature Aggregation Module (FAM ) to aggregate features extracted by the backbone network, thereby enhancing the strength of dual-temporal feature representation. The effectiveness and robustness of our proposed method have been validated on two widely used public datasets.