Using color-only vegetation indexes to remove vegetation from otherwise mostly mono-material point clouds

engineering disciplines, whether they are man-made objects such as
structures, buildings, or various types of structures. Commonly used
methods of acquiring such data include ground, UAV, or even aerial
photogrammetry, followed by terrestrial, UAV, and aerial scanning.
After measurement (by the scanner) or calculation (from
photogrammetry), the point cloud goes through extensive processing
that essentially transforms the unordered mass of points into a usable
data set. One of the important steps is removing points representing
obstructing objects and features, including vegetation in particular.
Here, many filtering methods based on different principles are
available and suitable for application to different scenes.
This paper presents a new method of filtering point clouds based on
the visible spectrum color principle using vegetation indexes
determined from RGB system colors only. Since each sensor has to
some extent, an individual interpretation of the colors, it cannot be
assumed to determine specific boundaries of what is and is no longer
vegetation. Therefore, it was proposed to use means clustering to
simplify the operator's work. The method was also designed in such
a way that the entire evaluation could be implemented in the freely
available CloudCompare software.
The procedure was tested on three different sites with different terrain
and vegetation characteristics showing, which demonstrated the
applicability of this method to data where the color information
(green) uniquely identifies vegetation. The selected vegetation filters
ExG, ExR, ExB, and ExGr were tested, where ExG was the best. Kmeans clustering helps an operator to distinguish more easily
between vegetation and the rest of the point cloud without
compromising the quality of the result. The method is practically
implementable using the freely downloadable and usable
CloudCompare software.