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Title:Zaznava oljnih madežev v multispektralnih satelitskih slikah : diplomsko delo
Authors:Kužner, Marko (Author)
Mongus, Domen (Mentor) More about this mentor... New window
Selčan, David (Co-mentor)
Rotovnik, Tomaž (Co-mentor)
Files:.pdf UN_Kuzner_Marko_2020.pdf (1,57 MB, This file will be accessible after 31.08.2023)
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V tem diplomskem delu predstavljamo analizo metod za zaznavanje oljnih madežev na vodni površini s satelitom TRISAT. Primerjali smo metodo največjega verjetja in nevronsko mrežo. Algoritma smo učili in testirali nad dvema različnima bazama podatkov. Z rezultati smo pokazali, da je metoda največjega verjetja računsko in prostorsko bolj spremenljiva pri manjšem številu vhodnih podatkov, medtem ko se je nevronska mreža izkazala za natančnejšo. S primerjavo najboljših izbranih kanalov nad bazama podatkov smo pokazali, da so si izbrani kanali podobni. Rezultate tega diplomskega dela lahko uporabimo za izvedbo algoritma nad referenčnimi slikami satelita TRISAT.
Keywords:satelit TRISAT, metoda največjega verjetja, nevronske mreže, zaznavanje olja, kratkovalovni infrardeči spekter
Year of publishing:2020
Place of performance:Maribor
Publisher:[M. Kužner]
Number of pages:X, 30 f.
Source:Maribor
UDC:004.93:629.783(043.2)
COBISS_ID:41195267 Link is opened in a new window
NUK URN:URN:SI:UM:DK:GGETQJ3T
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:202
Downloads:0
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Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Detection of oil spills in multispectral satellite images
Abstract:This diploma thesis explored methods for the detection of oil spills in multispectral satellite images. We compared the maximum likelihood classification and neural networks. Algorithms were trained and tested over two different databases. Results showed that the maximum likelihood classification is computationally and space complexity more suitable for a smaller number of inputs, while the neural network proved to be more accurate on the other side. A comparison of the best selected channels over the databases showed that selected channels were similar. The results of this diploma thesis can be used for the implementation of an algorithm on the reference images of satellite TRISAT.
Keywords:satellite TRISAT, maximum likelihood classification, neural networks, oil detection, short-wavelength infrared spectrum


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