TY  - JOUR
T1  - Classification of UAV point clouds by random forest machine learning algorithm
AU  - Zeybek, Mustafa
PY  - 2021
DA  - April
DO  - 10.31127/tuje.669566
JF  - Turkish Journal of Engineering
JO  - TUJE
PB  - Murat YAKAR
WT  - DergiPark
SN  - 2587-1366
SP  - 48
EP  - 57
VL  - 5
IS  - 2
LA  - en
AB  - Today, unmanned aerial vehicle (UAV)-based images have become an important data sources for researchers who deals with mapping from various disciplines on photogrammetry and remote sensing. Reconstruction of an area with three-dimensional (3D) point clouds from UAV-based images are an essential process to be used for traditional 2D cadastral maps or to produce a topographic maps. Point clouds should be classified since they subjected to various analyses for extraction for further information from direct point cloud data. Due to the high density of point clouds, data processing and gathering information makes the classification of point clouds a challenging task and may take a long time. Therefore, the classification processing allows an optimal solution to acquire valuable information.  In this study, random forest machine learning algorithm for classification processing is applied with radiometric features (Red band, Green band and Blue band) and geometric characteristics derived from covariance feature (curvature, omnivariance, flatness, linearity, surface variance, anisotropy and normalized terrain surface) of points. In addition, the case study is presented in order to test applicability of the proposed methodology to acquire an accuracy and performance of random forest method on the UAV based point cloud. After the classification processing, a class assigned each point from the model was compared with the reference data class.  Lastly, the overall accuracy of the classification was achieved as 96% and the Kappa index was reached to 91% on data set.
KW  - Unmanned aerial vehicle
KW  - Point cloud
KW  - Classification
KW  - Random forest
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UR  - https://doi.org/10.31127/tuje.669566
L1  - https://dergipark.org.tr/en/download/article-file/1093752
ER  -