Recently the high-resolution satellite images are helpful for managing the land cover, status data for the natural resources or environmental management. The effective satellite analysis process for these satellite images which requires more development to increase the effectiveness has become increasingly important. The satellite image has the advantage that you can quickly acquire the information of difficult access area, but the actual utilization rate is low. The classification result changed by user choice of classification method, the type of data, classification category. Therefore, a variety of remote sensing analysis has been an important topic for the researchers.
Support vector machines (SVMs) were originally designed for binary classification. And SVM binary classifications have 2 types of SVM, the difference is used optimization parameters C and . How to effectively extend it for multi-class classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multi-class classifier by combining several binary classifiers. Some authors also proposed a method that considers all classes at once, however SVM were originally designed for binary classification. In this study, we give decomposition implementations for two such“all-together”methods.
In the study, multi-class SVM methods are explained, and then SVM methods were applied to KOMPSAT-2 high-resolution satellite images. In the first step of the preprocessing was performed by through calculation and analysis of the statistical values of the training data in the study area. From the results, we found that each classification of the training data follows a normal distribution. Then Linear, Polynomial, RBF and Sigmoid kernel function of SVM methods by changing the parameters was applied in the study area. After that, the land cover result accuracy was analyzed, using reference map. Then we compared their performance with eight multi class SVM methods: “one-versus-all,” “one-versus-one,” and directed acyclic graph SVM with C or parameters, also "Weston & Watkins SVM" and "Crammer & Singer SVM" and those methods based on binary classification and consider the classes at once.
Our experiments indicated that the "Crammer & Singer SVM" and “one-versus-all” with C parameter are more suitable for practical use than the other methods in land cover classification. Also, we showed that Linear, Polynomial and RBF kernel functions are more suitable than Sigmoid kernel function.