Title: Vehicle-Focused Super Resolution of Remote Sensing Imagery
Language: English
Authors: Wahrmann, Patrick Markus Mathias 
Qualification level: Diploma
Advisor: Sablatnig, Robert  
Assisting Advisor: Zambanini, Sebastian 
Issue Date: 2021
Citation: 
Wahrmann, P. M. M. (2021). Vehicle-Focused Super Resolution of Remote Sensing Imagery [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.68780
Number of Pages: 86
Qualification level: Diploma
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.
Keywords: Super Resolution; Remote Sensing; Vehicle Detection
URI: https://doi.org/10.34726/hss.2021.68780
http://hdl.handle.net/20.500.12708/18603
DOI: 10.34726/hss.2021.68780
Library ID: AC16336784
Organisation: E193 - Institut für Visual Computing and Human-Centered Technology 
Publication Type: Thesis
Hochschulschrift
Appears in Collections:Thesis

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