MCFNet: Multiscale Cross-Modal Fusion Network for Remote Sensing Image Semantic Segmentation

Multimodal fusion methods have made great advancement in the field of remote sensing image segmentation in recent years. However, the efficient integration of local and global features from multiple modalities to improve the robustness of segmentation model remains a challenging task. In this paper, we propose a multiscale cross-modal fusion network (MCFNet) for semantic segmentation of high-resolution remote sensing images. This model employs a symmetrical dual-CNN encoder and a lightweight MLP decoder to learn comprehensive feature representation across diverse modalities. Specifically, to refine modality features at different stages, we construct a feature fusion module (FFM) that fuses rich complementary information by gradually aggregating local and global features. Additionally, a multiscale cross-modal feature correlation module (MCFCM) is introduced to deeply capture and correlate interactive feature information from the fused features. Finally, an efficient multiscale enhancement module (MEM) is leveraged to further enhance multiscale information extraction. Extensive comparison experiments on two benchmark datasets reveal that our method obtains superior performance compared with state-of-the-art methods.