Scale-adaptive segmentation and recognition of individual trees based on LiDAR data

Authors:
Roman M. Palenichka;Marek B. Zaremba
Affiliations:
Université du Québec en Outaouais Gatineau, Québec, Canada;Université du Québec en Outaouais Gatineau, Québec, Canada
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

Citing 5
Cited 0

Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention

International Journal of Computer Vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection

International Journal of Computer Vision
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Focusing attention on objects of interest using multiple matched filters

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.