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Remote Sens. 2017, 9(7), 676; https://doi.org/10.3390/rs9070676

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

1
Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Section Remote Sensing, Telegrafenberg, 14473 Potsdam, Germany
2
Geography Department, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany
3
Integrated Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Magaly Koch and Prasad S. Thenkabail
Received: 15 May 2017 / Revised: 27 June 2017 / Accepted: 27 June 2017 / Published: 1 July 2017
Full-Text   |   PDF [11374 KB, uploaded 1 July 2017]   |  

Abstract

Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better. View Full-Text
Keywords: image co-registration; Python; sub-pixel; Fourier shift theorem; optical; radar; phase correlation; geometric pre-processing; inter-sensor; intra-sensor image co-registration; Python; sub-pixel; Fourier shift theorem; optical; radar; phase correlation; geometric pre-processing; inter-sensor; intra-sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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.

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