MoViM: A Hybrid CNN Vision Mamba Network for Lightweight Semantic Segmentation of Multimodal Remote Sensing Images

The “Others” category in multimodal remote sensing images is characterized by high intra-class variability. Therefore, existing lightweight semantic segmentation models struggle with this category due to limitations in capturing both local details and global dependencies efficiently. We propose MoViM, a lightweight model that integrates a hybrid Vision Mamba (ViM) and CNN backbone to capture global contextual information and local details effectively. In addition, the MoViM also features an Inverted Stem for efficient multimodal fusion, a Global Semantics Extraction (GSE) module for enhanced global feature representation, and a Global-Local Feature Fusion (GLF) module for context-aware feature integration. Extensive experiments on WHU-OPT-SAR and Potsdam datasets demonstrate that MoViM achieves state-of-the-art performance, particularly in the “Others” category, while maintaining low computational complexity. Our codes are available at https://github.com/WenliangDu/MoViM.