MDAF-Net: Multi-Domain Adaptive Fusion Network for Multi-Source Remote Sensing Data Classification

📌 Overview

MDAF-Net is a novel multimodal fusion framework designed for joint classification of hyperspectral imaging (HSI) and LiDAR data.

The proposed network integrates:

• Multi-scale feature extraction
• Adaptive spatial-channel interaction
• Frequency-aware fusion

to fully exploit complementary information across:

• Spatial domain
• Spectral domain
• Frequency domain

Extensive experiments demonstrate that MDAF-Net achieves state-of-the-art performance on multiple public remote sensing datasets.

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👉 Data

We conducted 10 distinct data partitions based on IF_CALC implementation and adopted the average results across these iterations as the final reported outcomes in our study.

• Houston

• MUUFL

• Trento

🌈 Results

Dataset	OA (%)	AA (%)	Kappa (%)
Houston	96.02	96.63	95.70
MUUFL	85.61	85.08	81.39
Trento	99.51	98.96	99.34

🌿 Getting Started

Environment Setup

To get started, we recommend setting up a conda environment and installing dependencies via pip. Use the following commands to set up your environment.

conda create -n mdafnet python==3.11

conda activate mdafnet

pip install -r requirements.txt

Train and Test

python demo.py

Citation

If this code is useful for your research, please cite this paper.

@ARTICLE{song2026multi,
	title = {Multi-domain adaptive fusion network for multi-source remote sensing data classification},
	author = {Song, Qiya and Peng, Jianle and Song, Weiwei and Sun, Bin and Dian, Renwei and Li, Shutao},
	journal = {SCIENCE CHINA Information Sciences},
	year = {2026},
}

🌸 Acknowledgment

We are deeply grateful to repositories IF_CALC, GLT and FDNet, which served as the foundational basis for our code implementation.