20.500.12556/DKUM-83843 Preslikava stila satelitskih posnetkov s pomočjo generativnih nasprotniških nevronskih mrež magistrsko delo Transferring the style of satellite images using generative adversarial neural networks 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. 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. generativne nasprotniške mreže globoko učenje preslikava stila satelitski posnetki zemljevidi generative adversarial networks deep learning style transfer satellite imagery maps true true false [M. Lakič] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2023-02-21 19:08:52 2023-03-28 08:06:04 2023-05-13 03:06:20 0000-00-00 00:00:00 2023 0 Maribor 0 Maribor 1 spletni vir (1 datoteka PDF (X, 92 f.)) 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.032.26+004.85(043.2) 151855619 MAG_Lakic_Mitja_2023.pdf MAG_Lakic_Mitja_2023.pdf 1 262610C6311F9D19594707D6611DE0E6 c5c3e0c3ac3f0da2ff95245e153e6ca6116ee2cf82c77e75f903d03a167396c9 cd1f8a89-b217-11ed-879a-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=165794 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0