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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/

Abstract

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
Authors:
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 SCOPUS:Yes
In ISI Web of Science:No
DOI :10.1109/JURSE.2017.7924548
Page Range:pp. 1-4
Status:Published
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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