Classification of 3D UAS-SfM Point Clouds in the Urban Environment
Abstract
The classification of three-dimensional (3D) point clouds derived using cost-effective and time-efficient photogrammetric technologies can provide helpful information for applications, particularly in the mapping context. This paper presents a practical study of 3D Unmanned Aerial System (UAS) – Structure-from-Motion (SfM) point cloud classification using mainly open-source software. Following a supervised classification approach that utilizes only the dimensionality of points, the entire scene was classified into three land cover categories: ground, high vegetation, and buildings. The competence of classifying a 3D point cloud of a heterogeneous scene situated in the University of KwaZulu-Natal, South Africa, using the above approach has been evaluated. The resulting overall classification accuracy of 81.3% with a Kappa coefficient of 0.70 was determined using a confusion matrix. The results achieved indicate the potential use of open-source software and 3D UAS-SfM point cloud classification in mapping, monitoring complex environments, and other applications that may arise