期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 1
出版社：Copernicus Publications
摘要：LIght Detection And Ranging (LIDAR) is a powerful remote sensing technology in the acquisition of the terrain surface information for object classification and extraction. Major benefits of this technique are its high level of automation during data capturing and its spatial resolution. Because of high complexities and difficulties in urban areas, many researchers focus on the using of LIDAR data in such area. Consequently, one of the challenging issues about LIDAR data is classification of these data in urban area for identification of different objects such as building, road and tree. Several urban classification methods have been proposed for classification of LIDAR data. Support Vector Machines (SVM), one of the new techniques for pattern classification; have been widely used in many application areas such as remote sensing. SVM is a binary classification method but in some researches like remote sensing or pattern recognition, we need more than two classes. One solution for this difficulty is to split the problem into a set of binary classification before combining them. Multi-class SVM is one solution for solving mentioned problem. The one- against-one and the one-against-all are the two most popular strategies for Multi-class SVM. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Algorithm Multi-Class SVM (GASVM), that uses genetic algorithm as a method for kernel's parameter optimization for one of the Multi-class SVM classifiers. We have used genetic algorithm for optimizing γ and C parameters of RBF kernel in Multi-class SVM. The classification's results of LIDAR data by use of this presented technique clearly demonstrate higher classification accuracy
