# MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification

arXiv: https://arxiv.org/abs/2408.14255 · HTML: https://mlsanity.com/paper/2408.14255/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- First posted on arXiv 26 Aug 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Built on by (most cited first, 12 of 12)

- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification](https://mlsanity.com/paper/2503.06945/) (2025)
- [Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution](https://mlsanity.com/paper/2509.24334/) (2025)
- [Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification](https://mlsanity.com/paper/2509.23310/) (2025)
- [Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification](https://mlsanity.com/paper/2505.04003/) (2025)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [A Spatial-Spectral-Frequency Interactive Network for Multimodal Remote Sensing Classification](https://mlsanity.com/paper/2510.04628/) (2025)
- [Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.27155/) (2025)
- [CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2604.06844/) (2026)
- [Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation](https://mlsanity.com/paper/2605.16768/) (2026)
- [MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection](https://mlsanity.com/paper/2605.24928/) (2026)
- [Semantic-Guided Fusion Network for Multi-Source Remote Sensing Image Classification](https://mlsanity.com/paper/2609.27854/) (2026)

## Abstract (arXiv)

In the field of multi-source remote sensing image classification, remarkable progress has been made by using Convolutional Neural Network (CNN) and Transformer. Recently, Mamba-based methods built upon the State Space Model (SSM) have shown great potential for long-range dependency modeling with linear complexity, but they have rarely been explored for multi-source remote sensing image classification tasks. To address this issue, we propose the Multi-Scale Feature Fusion Mamba (MSFMamba) network, a novel framework designed for the joint classification of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR)/Synthetic Aperture Radar (SAR) data. The MSFMamba network is composed of three key components: the Multi-Scale Spatial Mamba (MSpa-Mamba) block, the Spectral Mamba (Spe-Mamba) block, and the Fusion Mamba (Fus-Mamba) block. The MSpa-Mamba block employs a multi-scale strategy to reduce computational cost and alleviate feature redundancy in multiple scanning routes, ensuring efficient spatial feature modeling. The Spe-Mamba block focuses on spectral feature extraction, addressing the unique challenges of HSI data representation. Finally, the Fus-Mamba block bridges the heterogeneous gap between HSI and LiDAR/SAR data by extending the original Mamba architecture to accommodate dual inputs, enhancing cross-modal feature interactions and enabling seamless data fusion. Together, these components enable MSFMamba to effectively tackle the challenges of multi-source data classification, delivering improved performance with optimized computational efficiency. Comprehensive experiments on four real-world multi-source remote sensing datasets demonstrate the superiority of MSFMamba outperforms several state-of-the-art methods. The source codes of MSFMamba are publicly available at https://github.com/oucailab/MSFMamba.