Multiscale Isotropic Matched Filtering for Individual Tree Detection in LiDAR Images

This paper addresses the issue of automated tree detection in remote-sensing imagery, particularly in the case of light detection and ranging (LiDAR) height data. The proposed method consists of multiscale isotropic matched filtering using a nonlinear image operator optimized for object detection and recognition. The method provides a robust scale- and orientation-invariant localization of the objects of interest. The local maxima of the matched-filtering operator are located at the potential centers of the objects of interest such as the trees. The tree verification stage consists of feature extraction at the candidate tree locations and comparison with the feature reference values. Experimental examples of the application of this matched-filtering method to LiDAR images of dense forest stands and sparsely distributed trees in residential areas are provided.