Laser Scanning Data Segmentation in Urban Areas by a Bayesian Framework

Abstract

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.