Abstract
The common method of LiDAR classifications is Markov random fields (MRF). Based on construction of MRF energy function, spectral and directional features are extracted for on-board urban point clouds. The MRF energy function is consisted of unary and pairwise potentials. The unary terms are computed by SVM classifictaion. The initial labeling is mainly processed through geometrical shapes. The pairwise potential is estimated by Naïve Bayes. From training data, the probability of adjacent objects is computed by prior knowledge. The final labeling method is reweighted message-passing to minimization the energy function. The MRF model is difficult to process the large-scale misclassification. We propose a super-voxel clustering method for over-segment and grouping segment for large objects. Trees, poles ground, and building are classified in this paper. The experimental results show that this method improves the accuracy of classification and speed of computation.
Highlights
With the development of technology, 3d sensors, such as laser sensors and cameras, are used for collecting space data
Using Naïve Bays classification, the pairwise terms are added into Markov random fields (MRF) model
In this paper we present an approach to classify the 3d point clouds for urban area
Summary
With the development of technology, 3d sensors, such as laser sensors and cameras, are used for collecting space data. The two main methods of the classification of point clouds are based on points and clusters. Associate Markov Network (AMN) (Daniel et al 2008) is applied to automatic-3d point cloud classification of urban environments. Daniel at 2009 proposes a high-order MRFs for classification He adds high–order cliques into the AMN model for efficient learning. Roman Shapovalov at 2010 presents a non-associate MRFs for 3d point cloud They uses the classic AMN model as a starting point and the general form of pairwise potentials to overtake the failure of AMN to detect both large and small objects due to over-smoothing. Ahmad Kamal Aijaz (2013) proposes the similar super-voxel clustering method and discusses the efficacy of the color and intensity for point clouds classification. In 4th section we show the experiment of the method and conclude the method at last
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