Laser scanning data segmentation in urban areas by a Bayesian framework

Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-27T11:22:23Z No. of bitstreams: 0Bitstream added on 2014-05-27T14:45:54Z : No. of bitstreams: 1 2-s2.0-36549018192.pdf: 990563 bytes, checksum: 2db10f19c763a71ca98bf6218ee934d3 (MD5) Made available in DSpace on 2014-05-27T11:22:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-01-01 In this paper is presented a region-based methodology for Digital Elevation Model segmentation obtained from laser scanning data. The methodology is based on two sequential techniques, i.e., a recursive splitting technique using the quad tree structure followed by a region merging technique using the Markov Random Field model. The recursive splitting technique starts splitting the Digital Elevation Model into homogeneous regions. However, due to slight height differences in the Digital Elevation Model, region fragmentation can be relatively high. In order to minimize the fragmentation, a region merging technique based on the Markov Random Field model is applied to the previously segmented data. The resulting regions are firstly structured by using the so-called Region Adjacency Graph. Each node of the Region Adjacency Graph represents a region of the Digital Elevation Model segmented and two nodes have connectivity between them if corresponding regions share a common boundary. Next it is assumed that the random variable related to each node, follows the Markov Random Field model. This hypothesis allows the derivation of the posteriori probability distribution function whose solution is obtained by the Maximum a Posteriori estimation. Regions presenting high probability of similarity are merged. Experiments carried out with laser scanning data showed that the methodology allows to separate the objects in the Digital Elevation Model with a low amount of fragmentation. Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Programa de Pós-Graduação em Ciências Cartográficas, Rua Roberto Simonsen, 305, Presidente Prudente, SP Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Departamento de Cartografia, Rua Roberto Simonsen, 305, Presidente Prudente, SP Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Departamento de Matemática, Estatística e Computação, Rua Roberto Simonsen, 305, Presidente Prudente, SP Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Programa de Pós-Graduação em Ciências Cartográficas, Rua Roberto Simonsen, 305, Presidente Prudente, SP Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Departamento de Cartografia, Rua Roberto Simonsen, 305, Presidente Prudente, SP Universidade Estadual Paulista Faculdade de Ciências e Tecnologia Departamento de Matemática, Estatística e Computação, Rua Roberto Simonsen, 305, Presidente Prudente, SP