Hypergraph-Driven Heterogeneous Spatial Relationship Learning for Remote Sensing Segmentation.

Remote sensing semantic segmentation is critical for extracting fine-grained spatial information in applications such as urban planning and environmental monitoring. However, existing methods face significant challenges in modeling heterogeneous spatial relationships within complex urban scenes, where semantically related regions are spatially dispersed yet functionally interdependent. Conventional convolutional neural networks exhibit limited receptive fields that fail to capture long-range dependencies, while Transformer-based approaches capture global dependencies but do not explicitly model regional heterogeneity, leading to blurred boundaries and category confusion. To address these limitations, this paper proposes a novel multi-relational-aware segmentation framework that leverages hypergraph theory to dynamically model higher-order semantic groupings across non-adjacent regions. The core innovation lies in a hypergraph structure learning unit that employs fuzzy clustering to partition multi-scale features into adaptive hyperedge sets, enabling joint representation of topological associations and functional dependencies among spatially distributed entities. Additionally, a multi-scale co-modeling strategy integrates stochastic feature masking with weighted fusion to bridge semantic abstraction and spatial localization. Experiments demonstrate that the proposed method achieves state-of-the-art mIoU performance on the LoveDA, Vaihingen, and Potsdam datasets, obtaining mIoU scores of 54.70%, 85.01%, and 87.64%, with improvements of 0.30%, 0.91%, and 0.08%, respectively, over the best existing methods.