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Open AccessArticle

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

1
German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany
2
Department of Computer Science University of Toronto, Toronto, ON M5S 3G, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Remote Sens. 2017, 9(6), 586; https://doi.org/10.3390/rs9060586
Received: 20 March 2017 / Revised: 19 May 2017 / Accepted: 29 May 2017 / Published: 10 June 2017
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches. View Full-Text
Keywords: geo-referencing; multi-sensor image matching; Siamese neural network; satellite images; synthetic aperture radar geo-referencing; multi-sensor image matching; Siamese neural network; satellite images; synthetic aperture radar
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Merkle, N.; Luo, W.; Auer, S.; Müller, R.; Urtasun, R. Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images. Remote Sens. 2017, 9, 586.

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