Multispectral Data Classification Based On Supprot Vector Machines

The theories and methods for high dimensional multispectral data classification are studied in the thesis based on support vector machines, which is an important part of research of the National Natural Science Foundation of China and the Natural Science Foundation of Hebei Province. The existing methods'ability of information extraction from spectral remote sensing images still largely lags behind technical developments. It is desirable and significant to study new theories and methods to improve this ability. Due to the limited number of training samples, high data dimension and the "Hughes Phenomenon", the performance of traditional pattern classification algorithms is often unsatisfactory. Statistical Learning Theory (SLT), the first theory that systematically studies the problem of machine learning with small size sample, presents a new inductive principle, structural risk minimization (SRM) principle, which can guide the selection of suitable classification model according to sample amount so as to obtain high generalization ability. Support vector machine (SVM) is a new general machine learning method based on SRM. In this thesis, several issues are addressed concerning the support vector machine and the classification of high dimensional multispectral data. The study is based on statistic learning theory (SLT) and support vector machine (SVM). The main work and results are outlined as follows:At first, the characteristics of high dimensional multispectral data are studied, and the weaknesses of the traditional pattern classification algorithms that deteriorate the performance are carefully analyzed. Appling statistic learning theory and support vector machine in high dimensional multispectral data classification, the Hughes phenomenon is reduced and higher classification accuracy is obtained.Secondly, five major types of multicategory support vector machine methods are systematically summarized and analyzed. These multicategory classification methods include: One-against-All, One-against-One, Directed Acyclic Graph SVMs (DAG-SVMs), Decision-Tree-Based Multiclass Support Vector Machines and Multiclass Support Vector Machines. Moreover, two types of Fuzzy Support Vector Machines are introduced and analyzed. Further, two improved fuzzy multicategory support vector machines are proposed and applied in classification of high dimensional multispectral data. They are based on the Multiclass Support Vector Machines method, and introduce the fuzzy membership of data samples of a given class so that to improve classification performance with high generalization capability.Thirdly, two types of Fuzzy Multicategory Support Vector Machines (FMSVM) based cn Support Vector Data Description (SVDD) are presented in order to reduce the effects of noises and outliers. The fuzzy membership is defined by not only the relation between a sample and its cluster center, but also the relation among samples. Two methods of defining the fuzzy membership are developed:One is based on the affinity among samples, and another is based on the improved Support Vector Data Description. The experimental results show that the presented two fuzzy multicategory support vector machines methods are more robust than the traditional support vector machine.Fourthly, in order to reduce the computational complexity, we propose a method for solving SVM inverse problems based on clustering. The computational complexity of SVM inverse problems by clustering is greatly reduced and the margin is enlarged. Based on the clustering, the relationship of the margin and the closest points in convex hulls can be also analyzed. For the linearly separable case, it is demonstrated that the maximum margin between the two subsets is equivalent to the distance of the two closest points in the convex hulls. For the inseparable case, the maximum margin between the two subsets is equivalent to the distance of the two closest points in the reduced convex hulls.Finally, the training algorithms of SVM for large-scale training set are summarized and analyzed. These methods include Chuncking, Decomposing and Sequential Minimal Optimization (SMO). We have used the improved Sequential Minimal Optimization to solve fuzzy multicategory support vector machine. The experimental results show that the computational load is greatly reduced and the generalization capability is improved.