NAPG: Neighborhood-Assisted Multiprototype Group Model for Cross-Domain Semantic Segmentation of Remote Sensing Images

Unsupervised domain adaptation (UDA) is crucial for semantic segmentation of remote sensing images (RS-SS), particularly when data distributions differ between source and target domains. Existing prototype-based UDA methods struggle with complex land cover class distributions and spatial information capture. To address these limitations, the Neighborhood-Assisted Multi-Prototype Group (NAPG) model is proposed. This model enhances cross-domain adaptability and spatial context richness by dynamically determining the number of prototype features and integrating neighborhood similarity gradients. Specifically, NAPG employs the Cross-Domain Representation of Multi-Prototype Group (CDR-MPG) module to generate multi-prototype group, capturing land cover complexity more effectively. Additionally, the Gradient Neighborhood Consistency Estimation (GNCE) module improves spatial representation by reducing intra-class variance and alleviating feature inconsistency. Experiments demonstrate that the proposed NAPG model outperforms state-of-the-art UDA methods across multiple datasets, achieving an mean intersection over union (mIoU) improvement of 3%. The source code is publicly available at https://github.com/Fanrongbo/NAPG-UDA-RS-SS.