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MMFNet: A Mamba-Based Multimodal Fusion Network for Remote Sensing Image Semantic Segmentation.

Jingting Qiu1, Wei Chang2,3, Wei Ren2,3

  • 1College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
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This study introduces MMFNet, a new multimodal fusion network for remote sensing semantic segmentation. It achieves high accuracy by combining local and global features, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Semantic segmentation of high-resolution remote sensing imagery faces challenges like intra-class variability, inter-class similarity, and single-modality data limitations.
  • Existing methods struggle to efficiently capture long-range dependencies and integrate multimodal information effectively.

Purpose of the Study:

  • To propose MMFNet, a novel multimodal fusion network for accurate and efficient semantic segmentation of remote sensing imagery.
  • To leverage the Mamba architecture for capturing long-range dependencies and fuse complementary information from optical imagery and Digital Surface Models (DSMs).

Main Methods:

  • MMFNet employs a dual-encoder architecture combining ResNet-18 for local details and VMamba for global context.
  • A Multimodal Feature Fusion Block (MFFB) integrates optical and DSM data, enhancing feature interaction.
Keywords:
feature fusionmultimodal semantic segmentationremote sensing

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  • A frequency-aware upsampling module (FreqFusion) is used in the decoder for improved boundary delineation.

    • Main Results:

    • MMFNet achieved mean Intersection over Union (IoU) scores of 83.50% on the ISPRS Vaihingen dataset and 86.06% on the Potsdam dataset.
    • The proposed network outperformed eight state-of-the-art methods in semantic segmentation accuracy.
    • MMFNet demonstrated competitive performance while maintaining relatively low computational complexity.

    Conclusions:

    • MMFNet offers an effective solution for accurate and efficient multimodal semantic segmentation in remote sensing.
    • The fusion of local and global features, along with multimodal data integration, significantly enhances segmentation performance.
    • The Mamba architecture shows promise for capturing long-range dependencies in remote sensing applications.