MeViT

MeViT: A Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery in Thailand

Author: Teerapong Panboonyuen (also known as Kao Panboonyuen)
Project: MeViT: A Medium-Resolution Vision Transformer
Publication: MeViT: A Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

Abstract

Semantic segmentation is a crucial task in remote sensing, focused on classifying every pixel within an image for various land use and land cover (LULC) applications. This project introduces MeViT (Medium-Resolution Vision Transformer), a novel approach tailored for Landsat satellite imagery of key economic crops in Thailand, including para rubber, corn, and pineapple. Our method enhances Vision Transformers (ViTs) by integrating medium-resolution, multi-branch architectures, optimized for semantic segmentation. The revised MixCFN (Mixed-Scale Convolutional Feedforward Networks) block within MeViT incorporates multiple depth-wise convolution paths, effectively balancing performance and efficiency.

Through extensive experimentation on Thailand’s satellite scenes, MeViT has demonstrated superior performance over state-of-the-art deep learning methods, achieving a precision of 92.22%, recall of 94.69%, F1 score of 93.44%, and mean IoU of 83.63%.

Key Features

Medium-Resolution Vision Transformer: Enhances standard ViTs by introducing a multi-branch architecture optimized for medium-resolution data.
Revised MixCFN Block: Integrates multiple depth-wise convolution paths to efficiently capture multi-scale local information.
State-of-the-Art Performance: Achieves top metrics in semantic segmentation tasks on Landsat imagery, outperforming existing models.

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/kaopanboonyuen/MeViT.git
cd MeViT
pip install -r requirements.txt

Usage

Configure Settings: Adjust the config.yaml file with your specific dataset paths and parameters.
Training: Train the model using the following command:
```
 python train.py
```
Evaluation: Assess the model’s performance:
```
 python evaluate.py
```
Inference: Generate segmentation maps using:
```
 python inference.py
```

Data

Landsat Satellite Imagery: The data used in this project is not included in the repository. Please refer to the project’s documentation for details on acquiring and preparing the necessary datasets.

Project Website

For more details, visit the project website.

Citation

If you use this project in your research, please cite our work:

@article{panboonyuen2023mevit,
  title={MeViT: A Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand},
  author={Panboonyuen, Teerapong and Charoenphon, Chaiyut and Satirapod, Chalermchon},
  journal={Remote Sensing},
  volume={15},
  number={21},
  pages={5124},
  year={2023},
  publisher={MDPI}
}

License

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

Acknowledgments

This work is based on research presented at a conference. Special thanks to our collaborators and contributors who supported the development of MeViT.

For any questions or contributions, feel free to open an issue or submit a pull request. We appreciate your interest in our work!

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