satellighte 0.0.2

Satellighte

Satellite Image Classification

Website • Docs • Demo

About The Satellighte

Satellighte is an image classification library that consist state-of-the-art deep learning methods. It is a combination of the words 'Satellite' and 'Light', and its purpose is to establish a light structure to classify satellite images, but to obtain robust results.

Satellite image classification is the most significant technique used in remote sensing for the computerized study and pattern recognition of satellite information, which is based on diversity structures of the image that involve rigorous validation of the training samples depending on the used classification algorithm.

Source: paperswithcode

Prerequisites

Before you begin, ensure you have met the following requirements:

Installation

To install Satellighte, follow these steps:

From Pypi

pip install satellighte

From Source

git clone https://github.com/canturan10/satellighte.git
cd satellighte
pip install .

Usage Examples

A few motivating and useful examples of how your project can be used. Spice this up with additional screenshots, code examples and demos.

For more examples, please refer to the Documentation

Tests

During development, you might like to have tests run.

Install dependencies

pip install -e ".[test]"

Linting Tests

pytest satellighte --pylint --pylint-error-types=EF

Document Tests

pytest satellighte --doctest-modules

Coverage Tests

pytest --doctest-modules --cov satellighte --cov-report term

Contributing

To contribute to Satellighte, follow these steps:

1. Fork this repository.
2. Create a branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the original branch: git push origin
5. Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.

Contributors

Oğuzcan Turan

You ?

Contact

If you want to contact me you can reach me at can.turan.10@gmail.com.

License

This project is licensed under MIT license. See LICENSE for more information.

References

The references used in the development of the project are as follows.

• Img Shields
• GitHub Pages
• FastFace
• PyTorch Lightning

Citations

@article{helber2019eurosat,
  title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2019},
  publisher={IEEE}
}

@inproceedings{helber2018introducing,
  title={Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
  pages={204--207},
  year={2018},
  organization={IEEE}
}

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