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Title:Zaznava oljnih madežev v multispektralnih satelitskih slikah : diplomsko delo
Authors:ID Kužner, Marko (Author)
ID Mongus, Domen (Mentor) More about this mentor... New window
ID Selčan, David (Comentor)
ID Rotovnik, Tomaž (Comentor)
Files:.pdf UN_Kuzner_Marko_2020.pdf (1,57 MB)
MD5: 6F595C4AFAB21ACABABF2DAF3F1FA798
PID: 20.500.12556/dkum/0d326f7f-5459-498a-9ff9-8c6f736cfa15
 
Language:Slovenian
Work type:Bachelor thesis/paper
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
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[M. Kužner]
Year of publishing:2020
Number of pages:X, 30 f.
PID:20.500.12556/DKUM-77465 New window
UDC:004.93:629.783(043.2)
COBISS.SI-ID:41195267 New window
NUK URN:URN:SI:UM:DK:GGETQJ3T
Publication date in DKUM:01.12.2020
Views:1584
Downloads:30
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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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:31.08.2020

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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