Object-Based Classification for LiDAR Point Cloud
Date Issued
2012
Date
2012
Author(s)
Lin, Keng-Fan
Abstract
Recently, with the development of high-resolution images, the image analysis and classification methods have transferred from pixel-based to object-based. Under the consideration of the specific spatial features of objects, such as spectral, shape or texture, or the subordinative relations among objects, object-based image analysis (OBIA) could give assistance to the description of object attributes, which effectively improves the image classification efficiency. In order to raise the capability of automatic recognition of land features from LiDAR data, the 2D object-based classification method is extended for 3D point cloud classification of LiDAR data in this study. The methodology is mainly divided into three parts. First of all, point cloud is segmented to independent 3D objects by various methods. Secondly, in order to describe the spatial characters of these objects, 3D features designed by this study are calculated (e.g., Model Ratio, Znormalized, PFH Index, Difference Ratio). At last, a set of decision rules is built and the point clouds are classified automatically by using the decision tree. To verify the applicability of various LiDAR data, airborne LiDAR and ground-based LiDAR were applied to automatic land feature classification in this study. Meanwhile, LASTOOL, software for LiDAR processing, and manual way were applied to classify the land features in experimental area as bases of parallel comparison and quality assessment. On the part of airborne LiDAR, structures, trees and cars were chosen to be the targets of classification. The overall accuracy and kappa value ran up to 98.40 % and 0.9638 respectively. On the part of ground-based LiDAR, buildings, small structures, trees, trunks and groves were chosen to be the targets. The overall accuracy and kappa value were 84.28 % and 0.7221 respectively. The results show that utilizing the object-based concept to classify LiDAR point cloud can assist point cloud recognition by means of describing the spatial characters of those objects. It then, therefore, improves not only the cognitive consistency of human perception but also the classification quality.
Subjects
Object-Based Classification
Segmentation
Feature Extraction
Decision Rules
Type
thesis
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