Classification of vegetation in aerial LiDAR data

Abstract

This contribution summarises a doctoral dissertation which proposes an algorithm for the classification of vegetation points in aerial LiDAR data. The algorithm characterizes vegetated areas based on statistically large dispersion in elevations of points, and the context in which the points are located. The algorithm is able to classify vegetation in both rural and urban areas with an average F1 score of 97.9\% and 91.0\%, respectively. The point-clouds can contain different types of vegetation and various degrees of canopy densities.

Author Biography

Denis Horvat, University of Maribor

Researcher at the Laboratory for Geometric Modeling and Multimedia Algorithms

References

D. Horvat (2017) Algorithm for classification of vegetation in LiDAR data, it Doctoral dissertation, Faculty of Electrical Engineering and Computer Science, University of Maribor (in Slovene)

D. Mongus, N. Lukač and B. Žalik (2014) Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 93, pp. 145--156

N. Bouaynaya and Mohammed Charif-Chefchaouni (2008) Theoretical Foundations of Spatially-Variant Mathematical Morphology Part I: Binary Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 5, pp. 145--156

D. Horvat, B. Žalik and D. Mongus (2016) Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 116, pp. 1--14

Authors

  • Denis Horvat University of Maribor

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Published

09/12/2017

How to Cite

Horvat, D. (2017). Classification of vegetation in aerial LiDAR data. Informatica, 41(3). Retrieved from https://www.informatica.si/index.php/informatica/article/view/1604

Issue

Vol. 41 No. 3 (2017)

Section

Thesis summary

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