Table of Contents

1. About The Software
2. Installation
3. Usage
4. Contributing
5. License
6. Contact
7. Cite

About The Software

A ready to use Python package (scripts) with a trained ML model to classify Sentinel-2 L1C image. The Python package takes the L1C product path and produces an RGB image with six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other) at 20m resolution.

(back to top)

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Installation

Below is an example of how you can instruct on installing and setting up.

1. Clone the repo

   git clone https://github.com/kraiyani/Sentinel_2_image_scene_classifier.git

2. Goto Sentinel_2_image_scene_classifier directory and unzip

   cd Sentinel_2_image_scene_classifier
   

3. Install dependency

   pip3 install -r requirements.txt

(back to top)

Usage

Use this space to show useful examples of how a project can be used.

Sample Sentinel 2 L1C images can be found under sample_L1C folder.

1. Goto Project-Name directory

   cd Sentinel_2_image_scene_classifier
   

2. Run main.py as

   python3 main.py -i <inputdirectory> -o <outputdirectory>
   

3. Example

   python3 main.py -i sample_L1C/S2B_MSIL1C_20180115T112419_N0206_R037_T29SNC_20180115T133323.SAFE -o output/
   

4. Find classified file in output folder

   classified_S2B_MSIL1C_20180115T112419_N0206_R037_T29SNC_20180115T133323.SAFE.png
   
   post_processed_classified_S2B_MSIL1C_20180115T112419_N0206_R037_T29SNC_20180115T133323.SAFE.png
   
   

Classified image color codes are: Water as Blue, Shadow as Brown, Cirrus as light Purple, Cloud as White, Snow as Cyan and Other as Green.

(back to top)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

(back to top)

License

Distributed under the MIT License.

(back to top)

Contact

Kashyap Raiyani- k.raiyani@uninova.pt

(back to top)

Cite

This work can be cited as:

1. MDPI and ACS Style

Raiyani, K.; Gonçalves, T.; Rato, L.; Salgueiro, P.; Marques da Silva, J.R. Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach. Remote Sens. 2021, 13, 300. https://doi.org/10.3390/rs13020300

2. AMA Style

Raiyani K, Gonçalves T, Rato L, Salgueiro P, Marques da Silva JR. Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach. Remote Sensing. 2021; 13(2):300. https://doi.org/10.3390/rs13020300

3. Chicago/Turabian Style

Raiyani, Kashyap, Teresa Gonçalves, Luís Rato, Pedro Salgueiro, and José R. Marques da Silva. 2021. "Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach" Remote Sensing 13, no. 2: 300. https://doi.org/10.3390/rs13020300

(back to top)