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Identifying corresponding patches in SAR and optical imagery with a convolutional neural network

Mou, Lichao and Schmitt, Michael and Wang, Yuanyuan and Zhu, Xiaoxiang (2017) Identifying corresponding patches in SAR and optical imagery with a convolutional neural network. IGARSS 2017, 23.-28. Juli 2017, Fort Worth, TX, USA.

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Official URL: http://www.igarss2017.org/

Abstract

In this paper, we investigate making use of a convolutional neural network (CNN) to solve the task of identifying corresponding patches in very high resolution (VHR) optical and SAR imagery of complicated urban scenery. By doing so, the binary decision function is learnt directly from automatically generated training data and does not resort to any hand-crafted features. First evaluations show great potential for further studies towards a generalized multi-sensor matching procedure.

Item URL in elib:https://elib.dlr.de/118274/
Document Type:Conference or Workshop Item (Speech)
Title:Identifying corresponding patches in SAR and optical imagery with a convolutional neural network
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Schmitt, Michaelm.schmitt (at) tum.deUNSPECIFIED
Wang, Yuanyuanyuanyuan.wang (at) dlr.deUNSPECIFIED
Zhu, Xiaoxiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:July 2017
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-4
Status:Published
Keywords:synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural net- works (CNN), image matching
Event Title:IGARSS 2017
Event Location:Fort Worth, TX, USA
Event Type:international Conference
Event Dates:23.-28. Juli 2017
Organizer:IEEE
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 > SAR Signal Processing
Deposited By: Mou, LiChao
Deposited On:18 Jan 2018 13:38
Last Modified:31 Jul 2019 20:15

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