Commit e0fb9566 authored by Daniel Scheffler's avatar Daniel Scheffler
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Revised README.rst.


Signed-off-by: Daniel Scheffler's avatarDaniel Scheffler <danschef@gfz-potsdam.de>
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.. figure:: http://danschef.gitext.gfz-potsdam.de/arosics/images/arosics_logo.png
:target: https://gitext.gfz-potsdam.de/danschef/arosics
An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data
**An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data**
* Free software: GNU General Public License v3
* Documentation: http://danschef.gitext.gfz-potsdam.de/arosics/doc/
* **Documentation:** http://danschef.gitext.gfz-potsdam.de/arosics/doc/
* The (open-access) **paper** corresponding to this software repository can be found here:
`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 Sensing. 2017; 9(7):676.
<http://www.mdpi.com/2072-4292/9/7/676>`__
Status
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:target: https://img.shields.io/pypi/pyversions/arosics.svg
See also the latest coverage_ report and the nosetests_ HTML report.
Detection and correction of local or global geometric displacements between two input images:
Feature overview
----------------
AROSICS is a python package to perform automatic subpixel co-registration of two satellite image datasets
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,
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. It supports a wide range of input data formats and can be used from the command
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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