Scale-Adaptive Segmentation and Recognition of Individual Trees Based on LiDAR Data

A scale-adaptive method for tree segmentation and recognition based on the LiDAR height data is described. The proposed method uses an isotropic matched filtering operator optimized for the fast and reliable detection of local and multiple objects. Sequential local maxima of this operator indicate the centers of potential objects of interest such as the trees. The maxima points also represent the seed pixels for the region-growing segmentation of tree crowns. The tree verification (recognition) stage consists of tree feature estimation and comparison with reference values. Various non-uniform tree characteristics are taken into account when making decision about a tree presence in the found location. Experimental examples of the application of this method for the tree detection in LiDAR images of forests are provided.

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