DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

On the Possibility of Conditional Adversarial Networks for Multi-Sensor Image Matching

Merkle, Nina and Fischer, Peter and Auer, Stefan and Müller, Rupert (2017) On the Possibility of Conditional Adversarial Networks for Multi-Sensor Image Matching. In: Proceedings of IGARSS 2017, pp. 1-4. IGARSS 2017, 23.-28. Jul. 2017, Fort Worth, Texas, USA.

[img] PDF


A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the combination of images acquired by different sensor types, e.g. active and passive, is a difficult task. Over the last years deep learning methods have proven their high potential for remote sensing applications. In this paper we will show how a deep learning method can be valuable for the problem of optical and SAR image matching. We investigate the possible of conditional generative adversarial networks (cGANs) for the generation of artificial templates. Contrary to common template generation approaches for image matching, the generation of templates using cGANs doesn't require the extraction of features. Our results show the possibility of realistic SAR-like template generation from optical images through cGANs and the potential of these templates for enhancing the matching of optical and SAR images by means of reliability and accuracy.

Item URL in elib:https://elib.dlr.de/115795/
Document Type:Conference or Workshop Item (Speech)
Title:On the Possibility of Conditional Adversarial Networks for Multi-Sensor Image Matching
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Merkle, Ninanina.merkle (at) dlr.deUNSPECIFIED
Fischer, Peterpeter.fischer (at) dlr.deUNSPECIFIED
Auer, Stefanstefan.auer (at) dlr.deUNSPECIFIED
Müller, Rupertrupert.mueller (at) dlr.deUNSPECIFIED
Date:January 2017
Journal or Publication Title:Proceedings of IGARSS 2017
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Keywords:conditional GANs, deep learning, image matching, multi-sensor, template generation
Event Title:IGARSS 2017
Event Location:Fort Worth, Texas, USA
Event Type:international Conference
Event Dates:23.-28. Jul. 2017
Organizer:IEEE GRSS
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:23 Nov 2017 13:52
Last Modified:31 Jul 2019 20:13

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.