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

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Abstract

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.

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Acknowledgments

The authors thank Prof. Andrea Fusiello for proofreading the paper and Dr. Daniele Piccolo for providing some statistical testing computations by the R package. Acknowledgements go to Kwang-Hua Fundation (Tongji University, Shanghai, People’s Republic of China). This research was partially funded by the National High-tech R&D Program of China (no. 2012AA121302), and by the National Basic Research Program of China (no. 2013CB733204).

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Authors and Affiliations

  1. Department of Civil Engineering and Architecture, University of Udine, via delle Scienze 206, 33100, Udine, Italy

    Fabio Crosilla, Dimitri Macorig & Domenico Visintini

  2. Centre for Spatial Information Science and Sustainable Development Applications, College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, 200092, Shanghai, People’s Republic of China

    Marco Scaioni

  3. Municipality of Tavagnacco (UD), Piazza Indipendenza 1, 33010, Feletto Umberto, Udine, Italy

    Ivano Sebastianutti

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  1. Fabio Crosilla
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  2. Dimitri Macorig
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  3. Marco Scaioni
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  4. Ivano Sebastianutti
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  5. Domenico Visintini
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Correspondence to Fabio Crosilla.

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Crosilla, F., Macorig, D., Scaioni, M. et al. LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories. Appl Geomat 5, 225–240 (2013). https://doi.org/10.1007/s12518-013-0113-9

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  • DOI: https://doi.org/10.1007/s12518-013-0113-9

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