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

IEEE TGRS 2025

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📌 Introduction

This repository contains the official implementation of our paper:
📄 MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification (IEEE TGRS 2025)

MSFMamba is an advanced multi-scale feature fusion model specifically designed for multi-source remote sensing image classification. By leveraging state-space modeling techniques, MSFMamba effectively captures both spatial and spectral dependencies, ensuring high accuracy and computational efficiency.

🔍 Key Features

🔍 Key Features:
✅ Multi-Scale Feature Extraction
✅ Cross-Modal Data Fusion
✅ State Space Model for Efficient Representation
✅ Enhanced Fusion for Multi-Source Remote Sensing Data

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📂 Dataset

The dataset used in our experiments can be accessed from the following link:
📥 Download Dataset Berlin and Augsburg

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🛠 Installation & Dependencies

Before running the code, make sure you have the following dependencies installed:

pip install causal-conv1d==1.1.1
pip install mamba-ssm==1.0.1

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🏋️‍♂️ Usage: Training MSFMamba

To train MSFMamba on the Berlin dataset, use the following command:

python train.py --epoch 40 --lr 1e-4 --batchsize 128 --dataset Berlin

🔧 Training Arguments:

• --epoch: Number of training epochs
• --lr: Learning rate
• --batchsize: Batch size
• --dataset: Dataset name

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📬 Contact

If you have any questions, feel free to contact us via Email:
📧 gaofeng@ouc.edu.cn
📧 jinxuepeng@stu.ouc.edu.cn

We hope MSFMamba helps your research! ⭐ If you find our work useful, please cite:

@article{msfmamba25,
  author={Gao, Feng and Jin, Xuepeng and Zhou, Xiaowei and Dong, Junyu and Du, Qian},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={MSFMamba: Multiscale Feature Fusion State Space Model for Multisource Remote Sensing Image Classification}, 
  year={2025},
  volume={63},
  pages={1-16}}