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/ | |||||||||||||||
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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: |
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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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