Hierarchical Bayesian analysis of high complexity data for the inversion of metric InSAR in urban environments [Elektronische Ressource] / von Marco Francesco Quartulli

Hierarchical Bayesian analysisof high complexity datafor the inversion of metric InSARin urban environmentsVom Fachbereich Elektrotechnik und Informatik derUniversit¨at Siegenzur Erkl¨arung des akademischen GradesDoktor der Ingenieurwissenschaften(Dr.-Ing.)genehmigte DissertationvonDipl.-Phys. Marco Francesco Quartulli1. Gutachter: Prof. Dr.-Ing. habil. O. Loffeld2. Gutachter: Prof. Dr.-Ing. habil. M. DatcuVorsitzender: Prof. Dr.-Rer.Nat. W. WiechertTag der mu¨ndlichen Pru¨fung:18 M¨arz 2005urn:nbn:de:hbz:467-1084AbstractIn this thesis, structured hierarchical Bayesian models and estimators are considered forthe analysis of multidimensional datasets representing high complexity phenomena.The analysis is motivated by the problem of urban scene reconstruction and understand-ingfrommeterresolutionInSARdata,observationsofhighlydiverse,structuredsettlementsthrough sophisticated, coherent radar based instruments from airborne or spaceborne plat-forms at distances of up to hundreds of kilometers from the scene.Based on a Bayesian analysis framework, stochastic models are developed for both theoriginalsignalstoberecovered(inthiscase,theoriginalscenecharacteristicsthatareobjectof the analysis— 3D geometry, radiometry in terms of cover type) and the noisy acquisitioninstrument (a meter resolution SAR interferometer).