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A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes

Mou, Lichao and Schmitt, Michael and Wang, Yuanyuan and Zhu, Xiao Xiang (2017) A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes. In: 2017 Joint Urban Remote Sensing Event, JURSE 2017, pp. 1-4. JURSE 2017, 6.-8.3.2017, Dubai, UAE. doi: 10.1109/JURSE.2017.7924548.

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Official URL: http://ieeexplore.ieee.org/document/7924548/


In this paper we propose a convolutional neural network (CNN), which allows to identify corresponding patches of very high resolution (VHR) optical and SAR imagery of complex urban scenes. Instead of a siamese architecture as conventionally used in CNNs designed for image matching, we resort to a pseudo-siamese configuration with no interconnection between the two streams for SAR and optical imagery. The network is trained with automatically generated training data and does not resort to any hand-crafted features. First evaluations show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development to a generalized multi-sensor matching procedure.

Item URL in elib:https://elib.dlr.de/118154/
Document Type:Conference or Workshop Item (Speech)
Title:A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Schmitt, Michaelm.schmitt (at) tum.deUNSPECIFIED
Wang, Yuanyuanyuanyuan.wang (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:March 2017
Journal or Publication Title:2017 Joint Urban Remote Sensing Event, JURSE 2017
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/JURSE.2017.7924548
Page Range:pp. 1-4
Keywords:convolutional neural network (CNN), optical and SAR imagery, corresponding patches.
Event Title:JURSE 2017
Event Location:Dubai, UAE
Event Type:international Conference
Event Dates:6.-8.3.2017
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Mou, LiChao
Deposited On:11 Jan 2018 15:35
Last Modified:31 Jul 2019 20:15

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