Abstract
This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.
Highlights
Commercial Airborne Laser Scanning (ALS) systems emerged in the mid-1990s for bathymetric and topographic applications
With the aid of direct geo-referencing technique, laser scanning equipment installed in the aircraft collect a cloud of laser range measurements for calculating the
We developed a protype framework for the proposed segment-based point cloud classification method using C++ language and Point Cloud Library (PCL) [63]
Summary
Commercial Airborne Laser Scanning (ALS) systems emerged in the mid-1990s for bathymetric and topographic applications. ALS technique has been effectively used for ground point detection [3,4,5,6,7], topographic mapping [8], 3D city modelling [9,10,11,12,13], object recognition [14,15,16], solar energy estimation [17], etc. Over the last two decades, significant contributions to the consolidation and extension of ALS data processing methods have been witnessed [1]. Among these processing methods, classifying the ALS data into categorical object instances is the first and most critical step for further data processing and model reconstruction [18]. A brief description of these existing methods is provided as follows
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