Abstract
Abstract. A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.
Highlights
We propose a multiple-primitives-based hierarchical classification method for 3D urban areas
A multiple-primitives-based hierarchical classification strategy is proposed in this paper
The ground points and building points are classified in the regular surfaces based on two semantic rules
Summary
Airborne Laser Scanning (ALS) point clouds have many benefits in contrast with the commonly used 2D remote sensing images for a variety of applications, such as ground point extraction (Sithole and Vosselman, 2004; Meng et al, 2010; Chen et al, 2016; Zhang and Lin, 2013; Yang et al, 2016), 3D city modelling (Sampath and Shan, 2007; Chen et al, 2014; Jarzgbek-Rychard and Borkowski, 2016; Sampath and Shan, 2010; Yu et al, 2016), etc. All the points in the input ALS point cloud are classified by the trained classifier In this procedure, a neighborhood of each point is required to be determined, when it computes features for each point. There are three types of neighborhoods, i.e., spherical neighborhood (Lee and Schenk, 2002), cylindrical neighborhood (Filin and Pfeifer, 2005), k-closest neighborhood (Linsen and Prautzsch, 2001) To determine these neighborhoods, a scale parameter, either a fixed radius or a constant value is required. A number of neighborhood optimizing methods (Guo et al, 2015; Mitra and Nguyen, 2003; Lalonde et al, 2005; Pauly et al, 2003; Belton and Lichti, 2006; Demantke et al, 2011; Weinmann et al, 2014) have been proposed. These neighborhood optimization methods are rather time-consuming (Wang et al, 2016), which is the main disadvantage of this kind of classification strategy
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