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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

Trädhöjdsbestämning med UAV-fotogrammetri och UAV-laserskanning : En jämförande studie för detektering av riskträd

Larsson, Alexander, Oscarsson, Olle January 2020 (has links)
UAV (Unmanned Aerial Vehicle) eller drönare används för insamling av geografisk data och fotografering av såväl företag, myndigheter och privatpersoner. Tekniken förenklar insamling av data över stora geografiska områden och kan utnyttjas för kartering, modellering och analysering som volymbestämning. Studien genomfördes med syftet att detektera trädhöjder ur punktmoln genererade med laserskanning och digital fotogrammetri från luften. Vidare undersöktes det vilken metod som gav det mest tillförlitliga resultatet samt om teknikerna var applicerbara för detektering av riskträd. Riskträd innebär i denna studie träd som utgör ett potentiellt hot mot viktig infrastruktur som till exempel kraftledningar. Numera sker datainsamlingen primärt via helikopter för identifiering av sådana träd. Genom att använda olika drönartekniker för datainsamlingen kan kostnaderna reduceras. Insamlingen av data genomfördes över ett glest barrskogsområde i Rörberg strax utanför Gävle. Laserdata samlades in med en LiDAR (Light Detection and Ranging)-sensor från YellowScan monterad på en Geodrone X4L Professional-drönare och de fotogrammetriska data med en drönare av typen DJI Phantom 4 RTK (Real Time Kinematic) med standardkamera. För bägge insamlingarna georefererades insamlade data direkt genom enkelstations-RTK för laserskanningen och med SWEPOS Nätverks-RTK för den fotogrammetriska flygningen. För att kontrollera kvaliteten av insamlade data mättes sex stycken kontrollprofiler in med totalstation i skogspartiet. Dessa jämfördes sedan mot de skapade punktmolnen. Medelavvikelsen och standardavvikelsen mellan LiDAR och kontrollprofilerna fastställdes till -0,038 m och 0,049 m. För fotogrammetrin och kontrollprofilerna bestämdes medelavvikelsen till +0,060 m och standardavvikelsen 0,090 m. Dessa värden jämfördes sedan mot kraven i SIS-TS 21144:2016. För att bestämma absoluta höjder mättes tio stycken träd in med totalstation. Trädens högsta och lägsta punkter koordinatbestämdes och utifrån subtraktion erhölls absoluta värden för vilka höjder från LiDAR- och fotogrammetriskt framställda trädhöjdsmodeller kom att jämföras mot. Jämförelsen mellan metoderna visade en medelavvikelse på -0,325 m för LiDAR och -0,928 m för fotogrammetrin. Slutsatsen av denna studie visar att LiDAR är den mest lämpade tekniken för detektering av trädhöjder och skapande av trädhöjdsmodeller. Detta baseras på erhållna höjdvärden, den digitala terrängmodellens kvalitet och den goda täckningen av punkter i plan och höjd för punktmolnet. / UAVs (Unmanned Aerial Vehicles) or drones are commonly used for collecting spatial data and aerial images by companies, state agencies and civilians. The UAV techniques makes collection of geodata easier for large areas and can be used for mapping, 3D modelling and other analyses, e.g. for volume determination. The aim of this study was to compare 3D point clouds generated from airborne laser scanning and digital photogrammetry for detecting heights of trees. It was also investigated which method produced the most reliable results and if these were applicable for detecting risk trees. The definition of risk trees in this study are trees that run the potential risk of damaging important infrastructure such as electric power transmission lines. Nowadays the collection of data is mainly conducted using helicopters for identifying the risk trees, but with UAV technologies costs can be significantly reduced. The collection of data was performed over a sparse coniferous forest area in Gävle, Sweden. Laser data was collected using a YellowScan LiDAR (Light Detection and Ranging) sensor mounted on a drone. For the photogrammetric data, a DJI Phantom 4 RTK (Real Time Kinematic) drone was used with its standard camera. Both techniques were directly georeferenced using Single station RTK and SWEPOS Network RTK respectively. To check the quality of the collected data, six control profiles were established using a total station. These measurements were then compared to the generated point clouds. Our results show that the mean deviation and standard deviation in height between LiDAR point clouds and the control profiles are -0,038 m and 0,049 m, respectively. The mean deviation and standard deviation for photogrammetric point clouds and control profiles are +0,060 m and 0.090 m, respectively. These values were then compared to the requirements in SIS-TS 21144:2016. To determine absolute tree heights, ten random trees were measured using a total station. The coordinates of the highest and lowest points of each tree were then subtracted to serve as absolute height values. The comparison of the two UAV methods showed mean height deviations of   -0,325 m for LiDAR and -0,928 m for the photogrammetry. This study concludes that LiDAR is the most suitable technology of the two methods tested for detecting tree heights and creating canopy height models. This is based on the obtained height values, the quality of the digital terrain model and the good distribution of points in plane and height for the point cloud.
12

Utility-Preserving Face Redaction and Change Detection For Satellite Imagery

Hanxiang Hao (11540203) 22 November 2021 (has links)
<div><div><div><p>Face redaction is needed by law enforcement and mass media outlets to guarantee privacy. In this thesis, a performance analysis of several face redaction/obscuration methods, such as blurring and pixelation is presented. The analysis is based on various threat models and obscuration attackers to achieve a comprehensive evaluation. We show that the traditional blurring and pixelation methods cannot guarantee privacy. To provide a more secured privacy protection, we propose two novel obscuration methods that are based on the generative adversarial networks. The proposed methods not only remove the identifiable information, but also preserve the non-identifiable facial information (as known as the utility information), such as expression, age, skin tone and gender.</p><p>We also propose methods for change detection in satellite imagery. In this thesis, we consider two types of building changes: 2D appearance change and 3D height change. We first present a model with an attention mechanism to detect the building appearance changes that are caused by natural disasters. Furthermore, to detect the changes of building height, we present a height estimation model that is based on building shadows and solar angles without relying on height annotation. Both change detection methods require good building segmentation performance, which might be hard to achieve for the low-quality images, such as off-nadir images. To solve this issue, we use uncertainty modeling and satellite imagery metadata to achieve accurate building segmentation for the noisy images that are taken from large off-nadir angles.</p></div></div></div>

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