vhrharmonize: VHR Satellite Imagery Preprocessing Library

Important

This library is under active development and may change.

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Overview

vhrharmonize is an open-source Python library and CLI suite for preprocessing very high resolution (VHR) satellite imagery into analysis-ready products.

Current tested/supported end-to-end workflow: WorldView.
Additional providers and sensors (for example, Planet) are being added.

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Features

• Atmospheric correction workflows (Py6S default, FLAASH optional backend)
• RPC orthorectification
• Pansharpening
• Optional cloud masking with OmniCloudMask
• Tile utilities and tile-aware pairwise alignment (elastix)
• Batch scene discovery + metadata/override processing
• CLI and library-first interfaces

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Installation

1. Requirements

• Python >=3.9 (3.11+ recommended)
• GDAL-compatible environment
• 6S executable for Py6S workflows (default)
• ENVI Task Engine only for FLAASH workflows

2. Create environment (example)

conda create -n vhrharmonize python=3.11 -c conda-forge
conda activate vhrharmonize

3. Install package

git clone https://github.com/cankanoa/vhrharmonize.git
cd vhrharmonize
pip install -e .

Optional extras:

pip install -e ".[cloud]"      # omnicloudmask
pip install -e ".[elastix]"    # itk-elastix
pip install -e ".[py6s]"       # backward-compat extra; Py6S is included in base install
pip install -e ".[spectralmatch]"
pip install -e ".[docs]"

For conda users, install 6S with:

mamba install -n vhrharmonize -c conda-forge py6s sixs

Getting Started

For usage docs:

• docs/getting-started/quickstart.md
• docs/cli/overview.md

Primary CLI commands:

vhr-worldview --help
vhr-fetch-modis-water-vapor --help
vhr-cloudmask-raster --help
vhr-pansharpen-orthos --help
vhr-align-image-pair --help

Docs site:

python -m mkdocs serve -f docs/mkdocs.yml

Contributing

We welcome all contributions! To get started:

1. Fork the repository and create a new feature branch.
2. Make your changes.
3. Open a Pull Request against the main repository.

We appreciate any feedback, suggestions, or pull requests to improve this project.

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License

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