AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

Scheffler, Daniel
2025 · doi:10.5281/zenodo.17306532 · W7087101930
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AROSICS is an open-source Python package that automatically co-registers satellite imagery at the subpixel level using frequency domain matching and outlier detection for robust, multi-sensor results.

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The paper introduces AROSICS, an open-source Python software package for automatic subpixel co-registration of two multi-sensor satellite image datasets using a frequency-domain image matching approach within a multistage workflow to detect and correct false positives. It reports that the method can address both local and global misregistrations and is designed to be robust to common challenges of multi-sensor or multi-temporal imagery, with clouds handled via outlier detection and overlap automatically detected, while user masks can exclude areas from tie-point creation. The main caveat is that it is tailored to satellite image co-registration tasks rather than being validated for biomedical imaging or other data types. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

AROSICS is a python package to perform automatic subpixel co-registration of two satellite image datasets based on an image matching approach working in the frequency domain, combined with a multistage workflow for effective detection of false-positives. It detects and corrects local as well as global misregistrations between two input images in the subpixel scale, that are often present in satellite imagery. The algorithm is robust against the typical difficulties of multi-sensoral / multi-temporal images. Clouds are automatically handled by the implemented outlier detection algorithms. The user may provide user-defined masks to exclude certain image areas from tie point creation. The image overlap area is automatically detected. AROSICS supports a wide range of input data formats and can be used from the command line (without any Python experience) or as a normal Python package.
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AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data Authors/Creators - 1. GFZ Helmholtz Centre for Geosciences, Section 1.4 - Remote Sensing and Geoinformatics Contributors Researcher: - 1. GFZ Helmholtz Centre for Geosciences, Section 1.4 - Remote Sensing and Geoinformatics Description AROSICS is a python package to perform automatic subpixel co-registration of two satellite image datasets based on an image matching approach working in the frequency domain, combined with a multistage workflow for effective detection of false-positives. It detects and corrects local as well as global misregistrations between two input images in the subpixel scale, that are often present in satellite imagery. The algorithm is robust against the typical difficulties of multi-sensoral / multi-temporal images. Clouds are automatically handled by the implemented outlier detection algorithms. The user may provide user-defined masks to exclude certain image areas from tie point creation. The image overlap area is automatically detected. AROSICS supports a wide range of input data formats and can be used from the command line (without any Python experience) or as a normal Python package. Notes Files GFZ/arosics-v1.13.0.zip Files (23.9 MB) | Name | Size | Download all | |---|---|---| | md5:b95c6bea07bcaf36eea438ef52c7a8f2 | 23.9 MB | Preview Download | Additional details Related works - Is cited by - Journal article: https://www.mdpi.com/2072-4292/9/7/676 (URL) - Is documented by - Software documentation: https://danschef.git-pages.gfz-potsdam.de/arosics/doc (URL) - Is supplement to - Software: https://git.gfz-potsdam.de/danschef/arosics (URL) Software - Repository URL - https://github.com/GFZ/arosics References - Scheffler, D.; Hollstein, A.; Diedrich, H.; Segl, K.; Hostert, P. AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sens. 2017, 9, 676. doi:https://doi.org/10.3390/rs9070676

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