Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR

This paper proposes a new method for the detection of vegetation in LiDAR data. As vegetation points are characterised by non-linear distributions, they are efficiently recognised based-on large errors obtained when applying the local fitting of planar surfaces. In addition, three contextual filters are introduced capable of dealing with those exceptions that do not conform with previous interpretations. Namely, they are designed for detecting overgrowing vegetation, small objects attached to the planar surfaces (such as balconies, chimneys, and noise within the buildings) and small objects that do not belong to vegetation (vehicles, statues, fences). During the validation, the proposed method achieved over 97% correctness as well as completeness of vegetation recognition in rural areas while its average accuracy in urban settings was 90.7% in terms of F1-scores. The method uses only three input parameters and allows for efficient compensation between completeness and correctness, without significantly affecting the F1-score. Sensitivity analysis of the method also confirmed the robustness against a sub-optimal definition of the input parameters.