Aerial Image Semantic Segmentation Method Based on Cross-Modal Hierarchical Feature Fusion

Multimodal aerial image semantic segmentation enables fine-grained land cover classification by integrating data from different sensors, yet it remains challenged by information redundancy, inter-modal feature discrepancies, and class confusion in complex scenes. To address these issues, we propose a Cross-Modal Hierarchical Feature Fusion Network (CMHFNet) based on an encoder-decoder architecture. The encoder incorporates a Pixel-wise Attention-guided Fusion Module (PAFM) and a Multi-stage Progressive Fusion Transformer (MPFT) to suppress redundancy and model long-range intermodal dependencies and scale variations. The decoder introduces a residual information-guided feature compensation mechanism to recover spatial details and mitigate class ambiguity. Experiments on DDOS, Vaihingen, and Potsdam datasets demonstrate that CMHFNet surpasses state-of-the-art methods, validating its effectiveness and practical value.