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

Merkle, Nina and Wenjie, Luo and Auer, Stefan and Müller, Rupert and Urtasun, Raquel (2017) Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images. Remote Sensing, 9 (9), pp. 586-603. Multidisciplinary Digital Publishing Institute (MDPI). ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/9/6/586/pdf


Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like scene monitoring over time or the scene analysis after sudden events. These tasks often require the fusion of geo-referenced and precisely co-registered multi-sensor data. Images captured by high resolution synthetic aperture radar (SAR) satellites have an absolute geo-location accuracy within few decimeters. This renders SAR images interesting as a source for the geo-location improvement of optical images, whose geo-location accuracy is in the range of some meters. In this paper, we are investigating a deep learning based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data. Image registration between SAR and optical satellite images requires few but accurate and reliable matching points. To derive such matching points a neural network based on a Siamese network architecture was trained to learn the two dimensional spatial shift between optical and SAR image patches. The neural network was trained over TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The results of the proposed method confirm that accurate and reliable matching points are generated with a higher matching accuracy and precision than state-of-the-art approaches.

Item URL in elib:https://elib.dlr.de/111587/
Document Type:Article
Title:Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Merkle, Ninanina.merkle (at) dlr.deUNSPECIFIED
Wenjie, Luowenjie (at) cs.toronto.eduUNSPECIFIED
Auer, StefanStefan.Auer (at) dlr.dehttps://orcid.org/0000-0001-9310-2337
Müller, Rupertrupert.mueller (at) dlr.deUNSPECIFIED
Urtasun, Raquelurtasun (at) cs.toronto.eduUNSPECIFIED
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:No
Page Range:pp. 586-603
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:geo-referencing; multi-sensor image matching; Siamese neural network; satellite images; synthetic aperture radar;
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Merkle, Nina Marie
Deposited On:22 Mar 2017 16:05
Last Modified:21 Nov 2019 05:04

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