DMSF-Net: A Dual-Encoder Multi-Source Feature Fusion Network for Fine-Grained Urban Green Space Segmentation

The mapping and monitoring of urban green space (UGS) are of great significance for ecological assessment and sustainable development in urban settlements. However, for fine-grained classification tasks in high-resolution remote sensing imagery, existing methods suffer from significant challenges due to the high spectral similarity between low-growing and dense vegetation, as well as the complexity of spatial structures. To overcome these challenges, this paper proposes a Dual-Encoder Multi-Source Feature Fusion Network (DMSF-Net) for fine-grained urban green space segmentation. The proposed method constructs a parallel encoding structure for RGB and auxiliary features (NDVI and LBP), introduces an Adaptive Feature Fusion Module (AFFM) during the encoding phase to achieve dynamic weighted fusion of cross-source features, and designs a Boundary-Aware Up-Sampling Module (BAM) during the decoding phase to strengthen the representation of complex boundary regions through joint modeling of regional semantics and boundary information. Experimental results on a self-constructed UrbanGreen dataset and the publicly available Vaihingen dataset demonstrate the superior performance of DMSF-Net over existing mainstream methods across several evaluation metrics, achieving mIoU values of 82.27% and 74.73%, with improvements of 1.07% and 0.57% over the best baselines, respectively. The model demonstrates particularly strong discrimination capability for the fine-grained category of low vegetation. Ablation experiments further validate the usefulness of each structural module, with AFFM playing a key role in overall performance improvement, while the BAM improves boundary delineation as observed in visual comparisons. Through the synergistic integration of multi-source feature information and structural optimization, DMSF-Net effectively enhances fine-grained UGS segmentation in complex urban scenes, thereby providing an effective approach for high-resolution remote sensing-based urban ecological monitoring.