CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: COMPLEX SCENE ANALYSIS IN URBAN AREAS BASED ON AN ENSEMBLE CLUSTERING METHOD APPLIED ON LIDAR DATA P. Ramzi, F. Samadzadegan

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 COMPLEX SCENE ANALYSIS IN URBAN AREAS BASED ON AN ENSEMBLE CLUSTERING METHOD APPLIED ON LIDAR DATA P. Ramzi*, F. Samadzadegan Dept, of Geomatics Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran - (samadz, pramzi)@ut.ac.ir Commission III, WG II1/4 KEY WORDS: LIDAR, Feature, Object, Extraction, Training, Fusion, Urban, Building ABSTRACT: 3D object extraction is one of the main interests and has lots of applications in photogrammetry and computer vision. In recent years, airborne laser-scanning has been accepted as an effective 3D data collection technique for extracting spatial object models such as digital terrain models (DTM) and building models. Data clustering, also known as unsupervised learning is one of the key techniques in object extraction and is used to understand structure of unlabeled data. Classical clustering methods such as k-means attempt to subdivide a data set into subsets or clusters. A large number of recent researches have attempted to improve the performance of clustering. In this paper, the boost-clustering algorithm which is a novel clustering methodology that exploits the general principles of boosting is implemented and evaluated on features extracted from LiDAR data. This method is a multi clustering technique in which At each iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm such as k-means is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the weighted multiple clustering results. This clustering methodology is used for the analysis of complex scenes in urban areas by extracting three different object classes of buildings, trees and ground, using LiDAR datasets. Experimental results indicate that boost clustering using k-means as its underlying training method provides improved performance and accuracy comparing to simple k-means algorithm. 1. INTRODUCTION Airborne laser scanning also known as LiDAR has proven to be a suitable technique for collecting 3D information of the ground surface. The high density and accuracy of these surface points have encouraged research in processing and analyzing the data to develop automated processes for feature extraction, DEM generation, object recognition and object reconstruction. In LiDAR systems, data is collected strip wise and usually in four bands of first and last pulse range and intensity (Arefi et al, 2004). Clustering is a method of object extraction and its goal is to reduce the amount of data by categorizing or grouping similar data items together. It is known as an instance of unsupervised learning (Dulyakam and Rangsanseri, 2001). The grouping of the patterns is accomplished through clustering by defining and quantifying similarities between the individual data points or patterns. The patterns that are similar to the highest extent are assigned to the same cluster. Generally, clustering algorithms can be categorized into iterative square- error partitional clustering, hierarchical clustering, grid-based clustering and density-based clustering (Pedrycz, 1997; Jain et al., 2000). The most well-known partitioning algorithm is the k-means which is a partitional clustering method so that the data set is partitioned into k subsets in a manner that all points in a given subset are closest to the same center. In other words, it randomly selects k of the instances to represent the clusters. Based on the selected attributes, all remaining instances are assigned to their closer center. K-means then computes the new centers by taking the mean of all data points belonging to the same cluster. The operation is iterated until there is no change in the gravity centers. If k cannot be known ahead of time, various values of k can be evaluated until the most suitable one is found. The effectiveness of this method as well as of others relies heavily on the objective function used in measuring the distance between instances. The difficulty is in finding a distance measure that works well with all types of data (Jane and Dubes, 1995). Some attempts have been carried out to improve the performance of the k-means algorithm such as using the Mahalanobis distance to detect hyper-ellipsoidal shaped clusters or using a fuzzy criterion function resulting in a fuzzy c-means algorithm (Bezdek and Pal, 1992). A few authors have provided methods using the idea of boosting in clustering (Frossyniotis et al., 2004; Saffari and Bischof, 2007; Liu et al., 2008). 1.1 Related Work Boosting is a general and provably effective method which attempts to boost the accuracy of any given learning algorithm by combining rough and moderately inaccurate classifiers (Freund and Schapire, 1999). The difficulty of using boosting in clustering is that in the classification case it is straightforward whether a basic classifier performs well with respect to a training point, while in the clustering case this task is difficult since there is a lack of knowledge concerning the label of the cluster to which a training point actually belongs (Frossyniotis et al., 2004). The authors in (Frossyniotis et al., 2004) used the same concept, by using two different performance measures for assessing the clustering quality. They incorporated a very similar approach used in the original Discrete AdaBoost Corresponding author.

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