FPCNet-based change detection for remote sensing images

The objective of this study is to address semantic misalignment and insufficient accuracy in edge detail and discrimination detection, which are common issues in deep learning-based change detection methods relying on encoding and decoding frameworks. In response to this, we propose a model called FlowDual-PixelClsObjectMec (FPCNet), which innovatively incorporates dual flow alignment technology in the decoding stage to rectify semantic discrepancies through streamlined feature correction fusion. Furthermore, the model employs an object-level similarity measurement coupled with pixel-level classification in the PixelClsObjectMec (PCOM) module during the final discrimination stage, significantly enhancing edge detail detection and overall accuracy. Experimental evaluations on the change detection dataset (CDD) and building CDD demonstrate superior performance, with F1 scores of 95.1% and 92.8%, respectively. Our findings indicate that the FPCNet outperforms the existing algorithms in stability, robustness, and other key metrics.