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Title:MODELIRANJE SLIK SAR Z AVTO-BINOMSKIM MODELOM
Authors:Hebar, Marko (Author)
Čučej, Žarko (Mentor) More about this mentor... New window
Gleich, Dušan (Co-mentor)
Files:.pdf DR_Hebar_Marko_2010.pdf (37,81 MB)
 
Language:Slovenian
Work type:Dissertation (m)
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Doktorska disertacija predstavlja odpravljanje pegastega šuma z modeliranjem in izločanjem informacij iz posnetkov SAR (ang. Synthetic Aperture Radar - SAR), ki je izvedeno z Bayesovim sklepanjem. Novost, ki jo predstavljam v doktorski disertaciji je uporaba avto-binomskega modela pri Bayesovem sklepanju prvega reda, kjer ga za apriorno verjetnost uporabimo pri modeliranju posnetka. Verjetje v Bayesovem sklepanju prvega reda modelira pegasti šum, ki ga opišemo z gama porazdelitvijo. Odpravljanje pegastega šuma je izvedeno s cenilko največje verjetnosti MAP (ang. maximum a posteriori - MAP), ki jo je analitično zelo težko rešiti, zato z avto-binomskim modelom uporabimo aproksimacijo z diferencialom. Parametri avto-binomskega modela se določijo z Bayesovim sklepanjem drugega reda. Robovi v posnetku se določijo z algoritmom rasti regij. Glede na koeficient variacije so ločene homogene od heterogenih regije in adaptivno prilagajanje okolice avto binomskega modela. Eksperimentalni rezultati so pokazali, da predstavljena metoda zelo dobro modelira teksture in ima dobre lastnosti odpravljanja pegastega šuma in zelo dobro modelira teksture v realnih posnetkih SAR.
Keywords:Avto-binomski model, izločevanje informacij, maksimum a posteriori (MAP) cenilka, Synthetic aperture radar (SAR), ohranjevanje tekstur.
Year of publishing:2010
Publisher:[M. Hebar]
Source:Maribor
UDC:004.93:621.396.963
COBISS_ID:254665472 Link is opened in a new window
NUK URN:URN:SI:UM:DK:XUBSJKV0
Views:1979
Downloads:116
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:MODELING SAR IMAGES WITH AUTO-BINOMIAL MODEL
Abstract:This dissertation presents a model based despeckling of Synthetic Aperture Radar (SAR) images using Bayesian analysis. The use of first order of Bayesian inference with auto-binomial model is used for SAR image despeckling. The novelty of dissertation is an auto-binominal model, which models a prior probability density function (pdf). Speckle models the likelihood pdf, represented by Gamma distribution. An analytical solution of a maximum a posteriori (MAP) estimate using an auto-binominal prior is difficult to compute. Therefore, the approximate solution of MAP estimate is proposed using the differential. The best auto-binomial model is determined by the second order Bayesian inference. The edges in the SAR images are detected using region borders, which have statistically different properties. Coefficient of variation is used to distinguish between homogeneous and heterogeneous regions. The experimental results show that the proposed method is one of the best algorithms for the textural features extraction and its capability of speckle removing. The proposed despeckling method is comparable with state-of-the-art methods and well models textures presented in real SAR images.
Keywords:Auto-binomial model, information extraction, maximum a posteriori (MAP) estimation, Synthetic aperture radar (SAR), texture preservation.


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