Authors:
Farhad Samadzadegan
;
Mehdi Maboodi
;
Sara Saeedi
and
Ahmad Javaheri
Affiliation:
University of Tehran, Iran, Islamic Republic of
Keyword(s):
Clustering, LIDAR, K-Mean, FCM, SOM, Filtering, 3D Objects.
Abstract:
During the last decade airborne laser scanning (LIDAR) has become a mature technology which is now widely accepted for 3D data collection. Nevertheless, these systems have the disadvantage of not representing the desirable bare terrain, but the visible surface including vegetation and buildings. To generate high quality bare terrain using LIDAR data, the most important and difficult step is filtering, where non-terrain 3D objects such as buildings and trees are eliminated while keeping terrain points for quality digital terrain modelling. The main goal of this paper is to investigate and compare the potential of procedures for clustering of LIDAR data for 3D object extraction. The study aims at a comparison of K-Means clustering, SOM and Fuzzy C-Means clustering applied on range laser images. For evaluating the potential of each technique, the confusion matrix concept is employed and the accuracy evaluation is done qualitatively and quantitatively.