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Automated open source library to preprocess satellite imagery

Project description

vhrharmonize: VHR Satellite Imagery Preprocessing Library

License: MIT

[!IMPORTANT] This library is under active development and may change.


Overview

vhrharmonize is an open-source Python library and CLI suite for preprocessing very high resolution (VHR) satellite imagery into analysis-ready products. Current supported end-to-end workflows: WorldView-3 B1 imagery. Additional providers and sensors (for example, Planet) will be added.


Features

  • Atmospheric correction workflows (Py6S default, FLAASH optional backend)
  • RPC orthorectification with Orthority
  • Pansharpening with Orthority
  • Optional cloud masking with OmniCloudMask
  • Pairwise alignment (coregix)
  • Relative Radiometric Normalization with spectralmatch
  • WorldView scene discovery, IMD parsing, and standardized metadata mapping
  • CLI and library-first interfaces

Installation

See docs/getting-started/installation.md for detailed installation instructions or simply install like this:

conda create -n vhrharmonize -c conda-forge py6s sixs gdal python=3.11
conda activate vhrharmonize
pip install vhrharmonize[default]

Getting Started

For an overview of using the library see docs/getting-started/quickstart.md. The CLI can be usd by passing in arguments from a yaml file like this one docs/configs/worldview.example.yml and running:

vhr-worldview --config-yaml worldview.example.yml

Or pass in arguments directly from the command line:

vhr-worldview \
  --input-file-glob "/data/worldview/**/*.TIF" \
  --output-dir ../../processed \
  --run-alignment \
  --alignment-fixed-image /data/reference.tif

For detailed arguments use:

vhr-worldview --help
vhr-fetch-modis-water-vapor --help
vhr-flaash --help
vhr-cloudmask-raster --help
vhr-pansharpen-orthos --help
vhr-align-image-pair --help
vhr-orthorectification --help
vhr-radiometric-normalization --help
vhr-py6s --help

Contributing

We welcome all contributions! We appreciate any feedback, suggestions, or pull requests to improve this project. See docs/getting-started/contributing.md.


License

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

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