Fraunhofer) Classification of airborne 3D point clouds regarding separation of vegetation in complex environments
2021
Classification of outdoor point clouds is an intensely studied
topic, particularly with respect to the separation of vegetation from the
terrain and manmade structures. In the presence of many overhanging and
vertical structures, the (relative) height is no longer a reliable
criterion for such a separation. An alternative would be to apply
supervised classification; however, thousands of examples are typically
required for appropriate training. In this paper, an unsupervised and
rotation-invariant method is presented and evaluated for three datasets
with very different characteristics. The method allows us to detect planar
patches by filtering and clustering so-called superpoints, whereby the
well-known but suitably modified random sampling and consensus (RANSAC)
approach plays a key role for plane estimation in outlier-rich data. The
performance of our method is compared to that produced by supervised
classifiers common for remote sensing settings: random forest as learner
and feature sets for point cloud processing, like covariance-based
features or point descriptors. It is shown that for point clouds resulting
from airborne laser scans, the detection accuracy of the proposed method
is over 96% and, as such, higher than that of standard supervised
classification approaches. Because of artifacts caused by interpolation
during 3D stereo matching, the overall accuracy was lower for
photogrammetric point clouds (74–77%). However, using additional salient
features, such as the normalized green–red difference index, the results
became more accurate and less dependent on the data source.
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