Digital Image Processing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Effective fingerprint classification by localized models of support vector machines
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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Rapid advances in remote sensing sensor technology have made it recently possible to collect new dense 3D data like Light Detection And Ranging (LIDAR). One of the challenging issues about LIDAR data is classification of these data for identification of different objects in urban area like building, road, and tree. Regarding to complexities of objects in urban area and disability of LIDAR data to collect the radiometric information of surface, traditional classifiers have low level of performance in classification of LIDAR data. Combining classifiers is an established concept that it used for improvement of classification results. In this paper we propose a classifier fusion system scheme based on Support Vector Machine (SVM) for classification of LIDAR data. Different SVMs are trained on the best different subset of features that are proper for object extraction in LIDAR data and chosen by RANSAC as feature selection method. In this article, two multiclass SVM methods known as one-against-one and one-against-all are investigated for classification of LIDAR data and then final decision is achieved by Majority Voting method. The results confirm that established method on LIDAR data has improved accuracy of classification. It is also demonstrated that one-against-all results better accuracy comparing to one-against-one although it is much more time consuming.