Semantic Prompt and Graph-Convolution-Structure Distillation Framework for Semantic Segmentation of Remote Sensing Images

High-resolution remote sensing semantic segmentation plays a critical role in land-use monitoring, urban planning, and disaster response. However, its deployment remains challenging owing to modality heterogeneity, fine-scale object structures, and the high computational cost of current deep learning models. To address these challenges, we propose a semantic prompt and graph-convolution-structure distillation framework (SPGSNet-S ${}^{\ast } $ ), a compact, yet effective architecture that integrates multimodal feature enhancement with dual-path knowledge distillation (KD). Specifically, we design two lightweight modules-auxiliary spatial feature extraction (ASFE) and red-green-blue (RGB) representation-to denoise and align noisy normalized digital surface model (nDSM) features with RGB imagery, enabling robust feature fusion. In addition, we introduce a dual distillation scheme comprising graph-convolution-based structure distillation, which captures and transfers spatial topological dependencies, and semantic prompt distillation (SPD), which dynamically generates and injects class-aware visual prompts without external text supervision. Experimental results on the Vaihingen and Potsdam datasets show that SPGSNet-S ${}^{\ast }$ outperforms several state-of-the-art methods, achieving competitive performance with only 8.89 M parameters and 2.29 G floating-point operations (FLOPs). The source code and experimental results are publicly available at https://github.com/110-011/SPGSNet.