LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories

A new procedure supporting filtering and classification of LiDAR data based on both elevation and intensity analysis is introduced and validated. After a preliminary analysis to avoid the trivial classification of homogeneous datasets, a non-parametric estimation of the probability density function is computed for both elevation and intensity data values. Some statistical tests are used for selecting the category of data (elevation or intensity) that better satisfies a bi- or a multi-modal distribution. The iterative analysis of skewness and kurtosis is then applied to this category to obtain a first classification. At each step, the point with the highest value of elevation (or intensity) is removed. The classification is then refined by studying both statistical moments of the complementary data category, in order to look for potential sub-clusters. Remaining clusters are identified by applying the same iterative procedure to the still unclassified LiDAR points. For more complex point distribution shapes or for the classification of large scenes, a progressive analysis is proposed, which is based on the partitioning of the entire dataset into more sub-sets. Each of them is then independently classified by using the core procedure. Some numerical experiments on real LiDAR data confirmed the potentiality of the filtering/classification method.