文章基本信息

标题：EXTRAÇÃO AUTOMÁTICA DE CONTORNOS DE TELHADOS USANDO DADOS DE VARREDURA A LASER E CAMPOS RANDÔMICOS DE MARKOV
本地全文：下载
作者：Edinéia Aparecida dos Santos Galvanin ; Aluir Porfírio Dal Poz ; Aparecida Doniseti Pires de Souza 等
期刊名称：Boletim de Ciências Geodésicas
印刷版ISSN：1982-2170
出版年度：2008
卷号：14
期号：2
语种：Portuguese
出版社：Universidade Federal do Paraná-UFPR
摘要：This paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM´s showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified.
其他摘要：This paper proposes a methodology for automatic extraction of building roofcontours from a Digital Elevation Model (DEM), which is generated through theregularization of an available laser point cloud. The methodology is based on twosteps. First, in order to detect high objects (buildings, trees etc.), the DEM issegmented through a recursive splitting technique and a Bayesian mergingtechnique. The recursive splitting technique uses the quadtree structure forsubdividing the DEM into homogeneous regions. In order to minimize thefragmentation, which is commonly observed in the results of the recursive splittingsegmentation, a region merging technique based on the Bayesian framework isapplied to the previously segmented data. The high object polygons are extracted byusing vectorization and polygonization techniques. Second, the building roofcontours are identified among all high objects extracted previously. Taking intoaccount some roof properties and some feature measurements (e. g., area,rectangularity, and angles between principal axes of the roofs), an energy functionwas developed based on the Markov Random Field (MRF) model. The solution ofthis function is a polygon set corresponding to building roof contours and is foundby using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM´s showed that the methodologyworks properly, as it delivered roof contours with approximately 90% shapeaccuracy and no false positive was verified.
关键词：Automatic Extraction;Building Roof Contours;Digital Elevation Model;Laser Scanning Data;Markov Random Field;Extração Automática;Contornos de Telhados de Edifícios;Modelo Digital de Elevação;Dados de Varredura a Laser;Campos Randômicos de Markov