Doǧrusöz, E.Aksoy, S.2016-02-082016-02-082007http://hdl.handle.net/11693/26970Automatic extraction of buildings and modeling of their spatial arrangements provide essential information for urban applications. This paper describes our work on modeling urbanization using spatial building patterns. Building detection is done using Bayesian classification of multi-spectral information. The individual buildings are used as textural primitives, and co-occurrence based spatial domain features and Fourier spectrum-based frequency domain features are used to model their repetitiveness and periodicity at particular orientations. These features are used to classify image neighborhoods as organized (regular) and unorganized (irregular). Experiments with high-resolution Ikonos imagery show that the proposed technique can be used for automatic segmentation of urban scenes and extraction of valuable information about urban growth.TurkishAnd modelingApplications.Automatic extractionAutomatic segmentationsBayesian classificationBuilding detectionCo-occurrenceFourier spectrumsFrequency domain featuresHigh resolutionsIkonos imageryMulti-spectralSpatial arrangementsSpatial domainsUrban growthUrban scenesClassification (of information)Extractive metallurgyModel buildingsSecurity of dataSignal processingBuildingsConference Paper10.1109/SIU.2007.4298764