Classifier size optimisation in segmentation of three-dimensional point images of wood vegetation

Nikolsky I.M.
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The advent of laser scanning technologies has revolutionized forestry. Their use made it possible to switch from studying woodlands using manual measurements to computer analysis of stereo point images called point clouds.

Automatic calculation of some tree parameters (such as trunk diameter) using a point cloud requires the removal of foliage points. To perform this operation, a preliminary segmentation of the stereo image into the “foliage” and “trunk” classes is required. The solution to this problem often involves the use of machine learning methods.

One of the most popular classifiers used for segmentation of stereo images of trees is a random forest. This classifier is quite demanding on the amount of memory. At the same time, the size of the machine learning model can be critical if it needs to be sent by wire, which is required, for example, when performing distributed learning. In this paper, the goal is to find a classifier that would be less demanding in terms of memory, but at the same time would have comparable segmentation accuracy. The search is performed among classifiers such as logistic regression, naive Bayes classifier, and decision tree. In addition, a method for segmentation refinement performed by a decision tree using logistic regression is being investigated.

The experiments were conducted on data from the collection of the University of Heidelberg. The collection contains hand-marked stereo images of trees of various species, both coniferous and deciduous, typical of the forests of Central Europe.

It has been shown that classification using a decision tree, adjusted using logistic regression, is able to produce a result that is only slightly inferior to the result of a random forest in accuracy, while spending less time and RAM. The difference in balanced accuracy is no more than one percent on all the clouds considered, while the total size and inference time of the decision tree and logistic regression classifiers is an order of magnitude smaller than of the random forest classifier.

Keywords: laser scanning, point cloud, machine learning, segmentation
Citation in English: Nikolsky I.M. Classifier size optimisation in segmentation of three-dimensional point images of wood vegetation // Computer Research and Modeling, 2025, vol. 17, no. 4, pp. 665-675
Citation in English: Nikolsky I.M. Classifier size optimisation in segmentation of three-dimensional point images of wood vegetation // Computer Research and Modeling, 2025, vol. 17, no. 4, pp. 665-675
DOI: 10.20537/2076-7633-2025-17-4-665-675
URL: http://crm-en.ics.org.ru/journal/article/3635/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2025 Nikolsky I.M.

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