<div class="csl-bib-body">
<div class="csl-entry">Wahrmann, P. M. M. (2021). <i>Vehicle-focused super-resolution of remote sensing imagery</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.68780</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2021.68780
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/18603
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dc.description.abstract
Vehicle detection in remote sensing imagery has various applications in traffic analysis,planning, and rescue operations after natural disasters. Using super-resolution as apre-processing step to increase the spatial resolution of remote sensing imagery benefitsvehicle detection performance. This thesis proposes a novel procedure to train the superresolutionstep of this pipeline in a vehicle-focused manner, by cropping the trainingset to images centered around vehicles. The Residual Dense Network is selected assuper-resolution architecture and Faster R-CNN is utilized for vehicle detection. Six existing annotated datasets are combined and unified to create the vehicle-focused crops, a conventional dataset for super-resolution training, and a dataset for vehicledetection training. Additionally, testing on a seventh, completely unseen dataset allows a generalization error to be estimated. The effect of this super-resolution training methodon subsequent vehicle detection is quantified by training an identical super-resolution model on unfocused data for comparison. Extensive evaluation shows on par performance of the vehicle-focused approach, while allowing faster training.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Super Resolution
en
dc.subject
Remote Sensing
en
dc.subject
Vehicle Detection
en
dc.title
Vehicle-focused super-resolution of remote sensing imagery
en
dc.title.alternative
Super-Resolution von Luftbildern mit Fahrzeug-Fokus
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2021.68780
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Patrick Markus Mathias Wahrmann
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Zambanini, Sebastian
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tuw.publication.orgunit
E193 - Institut für Visual Computing and Human-Centered Technology