A hybrid model for classification of remote sensing images with linear SVM and support vector selection and adaptation

A Linear Support Vector Machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with a maximum margin. LSVM typically has higher classification accuracy for linearly separable data than nonlinearly separable data. For this type of data, Support Vector Selection and Adaptation (SVSA) method was developed that uses the support vectors obtained by LSVM and adapts them with respect to training dataset. However, the SVSA's classification performance may not be very satisfactory for linearly separable data in comparison to LSVM depending on the dataset used. In this paper, a hybrid model was presented that combines the results of LSVM and the SVSA efficiently. The main idea of the hybrid model is to utilize the performance of LSVM with the SVSA as LSVM model is already available during implementation of the SVSA. The method which uses the proposed hybrid model is called the Hybrid Support Vector Selection and Adaptation method (HSVSA). In order to show the effectiveness of the proposed model, one real multispectal and two hyperspectral dataset were experimented with the SVSA, LSVM, nonlinear SVM and the HSVSA in the classification. The results showed that when LSVM performs better than the SVSA, the HSVSA achieves LSVM's performance with the hybrid model, and vice versa.