OBJECT EXTRACTION FROM LIDAR DATA USING AN ARTIFICIAL SWARM BEE COLONY CLUSTERING ALGORITHM

Light Detection and Ranging (LIDAR) systems have become a standard data collection technology for capturing object surface information and 3D modeling of urban areas. Although, LIDAR systems provide detailed valuable geometric information, they still require extensive interpretation of their data for object extraction and recognition to make it practical for mapping purposes. A fundamental step in the transformation of the LIDAR data into objects is the segmentation of LIDAR data through a clustering process. Nevertheless, due to scene complexity and the variety of objects in urban areas, e.g. buildings, roads, and trees, clustering using only one single cue will not reach meaningful results. The multi dimensionality nature of LIDAR data, e.g. laser range and intensity information in both first and last echo, allow the use of more information in the data clustering process and ultimately into the reconstruction scheme. Multi dimensionality nature of LIDAR data with a dense sampling interval in urban applications, provide a huge amount of valuable information. However, this amount of information produces a lot of problems for traditional clustering techniques. This paper describes the potential of an artificial swarm bee colony optimization algorithm to find global solutions to the clustering problem of multi dimensional LIDAR data in urban areas. The artificial bee colony algorithm performs neighborhood search combined with random search in a way that is reminiscent of the food foraging behavior of swarms of honey bees. Hence, by integrating the simplicity of the k-means algorithm with the capability of the artificial bee colony algorithm, a robust and efficient clustering method for object extraction from LIDAR data is presented in this paper. This algorithm successfully applied to different LIDAR data sets in different urban areas with different size and complexities.

[1]  S. Kotsiantis,et al.  Recent Advances in Clustering : A Brief Survey , 2004 .

[2]  David P. Helmbold,et al.  Aerial Lidar Data Classification using AdaBoost , 2007, Sixth International Conference on 3-D Digital Imaging and Modeling (3DIM 2007).

[3]  Norbert Pfeifer,et al.  New Associate Editor pp iii-iv Segmentation of airborne laser scanning data using a slope adaptive neighborhood , 2006 .

[4]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[5]  Neal R. Harvey,et al.  Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  Andries P. Engelbrecht,et al.  Image Classification using Particle Swarm Optimization , 2002, SEAL.

[7]  Nicolas Monmarché,et al.  A new clustering algorithm based on the chemical recognition system of ants , 2002 .

[8]  G. Sithole,et al.  Recognising structure in laser scanning point clouds , 2004 .

[9]  Ajith Abraham,et al.  Swarm Intelligence in Data Mining (Studies in Computational Intelligence) , 2006 .

[10]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[11]  Peter Axelsson,et al.  Processing of laser scanner data-algorithms and applications , 1999 .

[12]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.

[13]  Rachida Amjoun,et al.  Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction , 2007, CW 2007.

[14]  Ajith Abraham,et al.  Swarm Intelligence in Data Mining , 2009, Swarm Intelligence in Data Mining.

[15]  Andries Petrus Engelbrecht,et al.  Differential evolution methods for unsupervised image classification , 2005, 2005 IEEE Congress on Evolutionary Computation.

[16]  Hans-Gerd Maas,et al.  The suitability of airborne laser scanner data for automatic 3D object reconstruction , 2001 .

[17]  Thomas Melzer,et al.  Non-parametric segmentation of ALS point clouds using mean shift , 2007 .

[18]  Andrew A. Millward,et al.  A comparison of hierarchical and partitional clustering techniques for multispectral image classification , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[19]  Nicolas Monmarché,et al.  Visual clustering based on chemical recognition system of ants , 2003 .

[20]  C. Briese,et al.  A NEW METHOD FOR BUILDING EXTRACTION IN URBAN AREAS FROM HIGH-RESOLUTION LIDAR DATA , 2002 .

[21]  Shi Zhong,et al.  A Comparative Study of Generative Models for Document Clustering , 2003 .

[22]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[23]  Shi Zhongzhi,et al.  A clustering algorithm based on swarm intelligence , 2001, 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479).

[24]  Karl Kraus.  Principles of airborne laser scanning , 2002 .

[25]  David Suter,et al.  Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction , 2007, 2007 International Conference on Cyberworlds (CW'07).

[26]  Sandra Paterlini,et al.  Differential evolution and particle swarm optimisation in partitional clustering , 2006, Comput. Stat. Data Anal..