Light Detection and Ranging (LiDAR) has attracted the interest of the research community in many fields, including object classification of the earth surface. In this paper we present an object-based classification method for airborne LiDAR that distinguishes three main classes (buildings, vegetation and ground) based only on LiDAR information. The key components of our proposal are the following: First, the LiDAR point cloud is stored in an octree for its efficient processing and the normal vector of each point is estimated using an adaptive neighborhood algorithm. Then, the points are segmented using a two-phase region growing algorithm where planar and non-planar objects are handled differently. The utilization of an epicenter point is introduced to allow regions to expand without losing homogeneity. Finally, a ruled-based procedure is performed to classify the segmented clusters. In order to evaluate our approach, a building detection was carried out, and results were obtained in terms of accuracy and computational time.
Jorge Martínez, Francisco F. Rivera, José C. Cabaleiro, David L. Vilariño, Tomás F. Pena, and David Miranda B., "A rule-based classification from a region-growing segmentation of airborne lidar," Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040F (Presented at SPIE Remote Sensing: September 27, 2016; Published: 18 October 2016); https://doi.org/10.1117/12.2240750.
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