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.

[1]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[2]  R. Dubayah,et al.  Lidar Remote Sensing for Forestry , 2000, Journal of Forestry.

[3]  Chengcui Zhang,et al.  A progressive morphological filter for removing nonground measurements from airborne LIDAR data , 2003, IEEE Trans. Geosci. Remote. Sens..

[4]  G. A. Blackburn,et al.  Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi‐spectral remotely sensed data , 2005 .

[5]  F. Gougeon.  A Crown-Following Approach to the Automatic Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images , 1995 .

[6]  Azriel Rosenfeld,et al.  Robust regression methods for computer vision: A review , 1991, International Journal of Computer Vision.

[7]  Johannes R. Sveinsson,et al.  Data fusion and feature extraction in the wavelet domain , 2003 .

[8]  Tomas Brandtberg.  Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America , 2003 .

[9]  Shu-Ching Chen,et al.  Automatic Construction of Building Footprints From Airborne LIDAR Data , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[11]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  Thomas Blaschke,et al.  A FULL GIS-BASED WORKFLOW FOR TREE IDENTIFICATION AND TREE CROWN DELINEATION USING LASER SCANNING , 2005 .

[14]  D. A. Hill,et al.  Combined high-density lidar and multispectral imagery for individual tree crown analysis , 2003 .

[15]  J. Pitkänen.  Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods , 2001 .

[16]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[17]  Carla E. Brodley,et al.  Focusing attention on objects of interest using multiple matched filters , 2001, IEEE Trans. Image Process..

[18]  Avideh Zakhor,et al.  Tree Detection in Urban Regions Using Aerial Lidar and Image Data , 2007, IEEE Geoscience and Remote Sensing Letters.

[19]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[20]  D. King,et al.  Development and evaluation of an automated tree detection-delineation algorithm for monitoring regenerating coniferous forests , 2005 .

[21]  Hemant D. Tagare,et al.  A Maximum-Likelihood Strategy for Directing Attention during Visual Search , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  P. Gessler,et al.  Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data , 2006 .

[23]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[24]  Jayaram K. Udupa,et al.  Optimum Image Thresholding via Class Uncertainty and Region Homogeneity , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Roman M. Palenichka,et al.  Multiscale model-based feature extraction in structural texture images , 2006, J. Electronic Imaging.