This repository contains the official implementation of our paper: "DMRS: Long-tailed remote sensing recognition via semantic-aware mixing and diversity experts"

📄 Paper: https://doi.org/10.1016/j.jag.2025.104623

We recommend using Python 3.11. Install dependencies using uv (recommended) or pip:

# Using uv (recommended)
uv sync

• PyTorch >= 2.7.1
• torchvision >= 0.22.1
• CLIP (OpenAI)
• PEFT >= 0.15.2
• scikit-learn >= 1.7.0
• matplotlib >= 3.10.3

Organize your remote sensing dataset in the following structure(The program will process it as long-tail data):

dataset/
├── class1/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
├── class2/
│   ├── image1.jpg
│   └── ...
└── ...

Supported datasets:

• NWPU-RESISC45
• RSD46-WHU
• Custom remote sensing datasets

python CLIP_Lora.py \
    --dataset_path ./NWPU-RESISC45 \
    --imb_type exp \
    --imb_factor 0.01 \
    --epochs 40 \
    --batch_size 16 \
    --lr 1e-1

python CLIP_Lora.py \
    --dataset_path ./NWPU-RESISC45 \
    --imb_type exp \
    --imb_factor 0.01 \
    --MME_loss True \
    --num_experts 3 \
    --mixrs True \
    --epochs 40 \
    --batch_size 16 \
    --lr 1e-1

• --dataset_path: Path to your dataset
• --imb_type: Type of imbalance ('exp' for exponential, 'step' for step)
• --imb_factor: Imbalance factor (0.01 for severe imbalance)
• --MME_loss: Enable D-LoRA loss function
• --num_experts: Number of experts (default: 3)
• --mixrs: Enable MixSSS data augmentation
• --lora_r: LoRA rank (default: 12)
• --lora_alpha: LoRA scaling factor (default: 24)

If you use this code in your research, please cite our paper:

@article{WANG2025104623,
    title = {DMRS: Long-tailed remote sensing recognition via semantic-aware mixing and diversity experts},
    journal = {International Journal of Applied Earth Observation and Geoinformation},
    volume = {141},
    pages = {104623},
    year = {2025},
    issn = {1569-8432},
    doi = {https://doi.org/10.1016/j.jag.2025.104623},
    url = {https://www.sciencedirect.com/science/article/pii/S1569843225002705},
    author = {Yifan Wang and Fan Zhang and Qihao Zhao and Wei Hu and Fei Ma},
    keywords = {Long-tail distribution, Remote sensing, Diversity experts, Data augmentation, Foundation models},
}

This project is licensed under the MIT License - see the LICENSE file for details.

For questions about the code or paper, please:

• Open an issue on GitHub
• Contact: [2024200827@buct.edu.cn]