Fine-Tuning SAM (Segment anything) for River Water Segmentation

Overview

This repository presents the Python code for fine-tuning the Segment Anything Model (SAM) to perform river water segmentation from close-range remote sensing imagery. This work is based on our paper published in IEEE Access:

A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," IEEE Access, 2024. IEEE Access

The easy-to-use and adaptable code for river water and other segmentation tasks and use for other remote sensing datasets:

Try it in Colab:

The LuFI-RiverSNAP.v1 (river water segmentation) Dataset in Google Drive:

Some examples of river water segmentation results on the LuFI-RiverSnap.v1. (a) Images and segmentation results generated by (b) U-Net_(ResNet50), (c) PSPNet_(ResNet50), (d) DeeplabV3+_(ResNet50), (e) PAN_(ResNet50), (f) LinkNet_(ResNet50), and (g) SAM were used as DL models for river water segmentation. Green: False Positives (FP) detection, Pink: False Negatives (FN) detection, Blue: correct detection of river water.

Try it in Colab:

Please also follow and read the reference codes we created for our fine-tuning SAM based on.

Original SAM Code: GitHub - Segment Anything

Fine-Tuning Tutorial: Encord Blog - Fine-Tune SAM

{Some examples of river water segmentation results on the LuFI-RiverSnap.\textit{v}1. (a) Images and segmentation results generated by (b) MobileSAM (TinyViT), (c) SAM (ViT-B), (d) and SAM (ViT-L)}

Dataset

The LuFI-RiverSNAP.v1 dataset for river water segmentation is available on multiple platforms:

• Kaggle: LuFI-RiverSNAP
• ISPRS ICWG III/IVa "Disaster Management" Datasets
• IEEE DataPort: LuFI-RiverSNAP

Try it in Colab:

Cite

Please cite the following papers if they help your research. You can use the following BibTeX entry:

@article{moghimi2024comparative,
  title={A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery},
  author={Moghimi, Armin and Welzel, Mario and Celik, Turgay and Schlurmann, Torsten},
  journal={IEEE Access},
  year={2024},
  doi={https://doi.org/10.48550/arXiv.2304.02643},
  publisher={IEEE}
}

A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," in IEEE Access, doi: 10.1109/ACCESS.2024.3385425. https://ieeexplore.ieee.org/document/10493013

@inproceedings{kirillov2023segment,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4015--4026},
  doi={https://doi.org/10.48550/arXiv.2304.02643},
  year={2023}
}

Contact

For any queries or contributions, feel free to contact us.