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Title:Preslikava stila satelitskih posnetkov s pomočjo generativnih nasprotniških nevronskih mrež : magistrsko delo
Authors:ID Lakič, Mitja (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf MAG_Lakic_Mitja_2023.pdf (5,49 MB)
MD5: 262610C6311F9D19594707D6611DE0E6
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu raziskujemo problematiko preslikave stila satelitskih posnetkov z uporabo generativnih nasprotniških nevronskih mrež (GAN). Najprej predstavimo osnovno strukturo nevronskih mrež, nato podrobneje opišemo generativne modele. Namen magistrskega dela je preveriti učinkovitost teh modelov pri preslikavi satelitskih posnetkov v stil zemljevida, kjer primerjamo dva različna GAN modela, in sicer Pix2Pix, ki spada med pogojne modele, in CycleGAN, ki je predstavnik cikličnih modelov. V okviru eksperimenta primerjamo pridobljene rezultate z uporabo teh modelov, kjer smo tudi preizkusili preslikavo v obratni smeri, torej iz zemljevida v stil satelitskega posnetka. Rezultati so pokazali, da je mogoče satelitske posnetke uspešno preslikati v stil zemljevida, kjer pogojni modeli na splošno zagotavljajo boljše rezultate, vendar so zelo odvisni od arhitekture omrežja. Magistrsko delo zaključimo z analizo rezultatov in odgovori na raziskovalna vprašanja.
Keywords:generativne nasprotniške mreže, globoko učenje, preslikava stila, satelitski posnetki, zemljevidi
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[M. Lakič]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (X, 92 f.))
PID:20.500.12556/DKUM-83843 New window
UDC:004.032.26+004.85(043.2)
COBISS.SI-ID:151855619 New window
Publication date in DKUM:28.03.2023
Views:612
Downloads:114
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
LAKIČ, Mitja, 2023, Preslikava stila satelitskih posnetkov s pomočjo generativnih nasprotniških nevronskih mrež : magistrsko delo [online]. Master’s thesis. Maribor : M. Lakič. [Accessed 4 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=83843
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:21.02.2023

Secondary language

Language:English
Title:Transferring the style of satellite images using generative adversarial neural networks
Abstract:In the master's thesis, we investigate the problem of transferring the style of satellite images using generative adversarial neural networks (GANs). First, we present the basic structure of neural networks, and then we describe generative models in more detail. The purpose of the master's thesis is to verify the effectiveness of these models in transferring satellite images into a map style, where we compare two different GAN models, namely Pix2Pix, which belongs to conditional models, and CycleGAN, which is a representative of cyclic models. As part of the experiment, we compare the results obtained using these models, where we also tested the style transfer in the reverse direction, that is, from a map to the style of a satellite image. The results showed that satellite imagery can be successfully transferred into a map style, where conditional models generally provide better results but are highly dependent on the network architecture. The master's thesis concludes with an analysis of the results and answers to the research questions.
Keywords:generative adversarial networks, deep learning, style transfer, satellite imagery, maps


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