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Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching

Merkle, Nina Marie and Auer, Stefan and Müller, Rupert and Reinartz, Peter (2018) Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (6), pp. 1811-1820. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2018.2803212 ISBN 1939-1404 ISSN 1939-1404

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Official URL: https://ieeexplore.ieee.org/document/8328024/?arnumber=8328024&source=authoralert

Abstract

Tasks such as the monitoring of natural disasters or the detection of change highly benefit from complementary information about an area or a specific object of interest. The required information is provided by fusing high accurate co-registered and geo-referenced datasets. Aligned high resolution optical and synthetic aperture radar (SAR) data additionally enables an absolute geo-location accuracy improvement of the optical images by extracting accurate and reliable ground control points (GCPs) from the SAR images. In this paper we investigate the applicability of a deep learning based matching concept for the generation of precise and accurate GCPs from SAR satellite images by matching optical and SAR images. To this end, conditional generative adversarial networks (cGANs) are trained to generate SAR-like image patches from optical images. For training and testing, optical and SAR image patches are extracted from TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The artificially generated patches are then used to improve the conditions for three known matching approaches based on normalized cross-correlation (NCC), SIFT and BRISK, which are normally not usable for the matching of optical and SAR images. The results validate that a NCC, SIFT and BRISK based matching greatly benefit, in terms of matching accuracy and precision, from the use of the artificial templates. The comparison with two state-of-the-art optical and SAR matching approaches shows the potential of the proposed method but also revealed some challenges and the necessity for further developments.

Item URL in elib:https://elib.dlr.de/118413/
Document Type:Article
Title:Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Merkle, Nina MarieNina.Merkle (at) dlr.dehttps://orcid.org/0000-0003-4177-1066
Auer, StefanStefan.Auer (at) dlr.dehttps://orcid.org/0000-0001-9310-2337
Müller, Rupertrupert.mueller (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:2018
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:11
DOI :10.1109/JSTARS.2018.2803212
Page Range:pp. 1811-1820
Editors:
EditorsEmail
UNSPECIFIEDIEEE Geoscience & Remote Sensing Society
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
ISBN:1939-1404
Status:Published
Keywords:Conditional generative adversarial networks (cGANs), multi-sensor image matching, artificial image generation, synthetic aperture radar (SAR), optical satellite images
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:30 Jan 2018 09:10
Last Modified:31 Jul 2019 20:15

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