Land Cover Classification of Optical–SAR Imagery via Cross-Modal Interaction and Feature Alignment

Abstract. Land cover classification (LCC) plays a crucial role in geoscientific research and resource monitoring applications. Compared with traditional single-modal classification methods, multimodal fusion models can more effectively leverage the complementary information of optical and synthetic aperture radar (SAR) imagery, thereby improving classification performance in complex scenarios. However, due to the significant differences in the imaging mechanisms of the two sensors, inconsistencies in radiometric properties and spatial structures arise between optical and SAR images, posing challenges for cross-modal feature interaction and fusion. To address this issue, we propose a multimodal optical–SAR fusion network (MOSFNet) for high-precision LCC, which incorporates two core modules: the Feature Interaction Module (FIM) and the Feature Fusion Module (FFM). The FIM achieves complementary feature interaction between optical and SAR images through channel splitting and cross concatenation, while incorporating a coordinate attention mechanism to enhance the responsiveness of key land cover regions. The FFM leverages a 2D selective scan (SS2D) mechanism to implement bidirectional cross-modal feature alignment and gated fusion in the hidden state space, enabling deep correlation and adaptive integration of optical and SAR features. Experiments on the WHU-OPT-SAR dataset demonstrate that MOSFNet significantly outperforms existing methods in terms of classification accuracy and model generalization, providing an efficient and robust solution for high-precision land cover mapping with multi-source remote sensing imagery.