Application of a new remote sensing image interpretation method in karst area - support vector machine algorithm

The existing methods of remote sensing image interpretation are unsupervised classification and supervised classification. The supervised classification includes parallel algorithm, the minimum distance algorithm and maximum likelihood algorithm. Support Vector Machine is a new supervised classification algorithm. In this study, some parts in the Huaxi District, Xiaobi Township in Guiyang is selected as the research object. Remote sensing images are interpreted by means of the maximum likelihood algorithm and Support Vector Machine algorithm respectively with SPOT data. Through establishing confusion matrix, calculating classification accuracy and Kappa coefficient, it is found that the classification accuracy of support vector machine is high and classification polygon is integrity. But to the time of consumption, the support vector machine is longer than the maximum likelihood algorithm. According to the two algorithms, there are both ground objects easy to be distinguished for their spectral features being quite different from other objects and some ground objects with similar spectrum easy to lead to misclassification. However, in terms of the classification accuracy, SVM classification is higher than the maximum likelihood. SVM is sensitive to the number of samples, so too much sample size will cause too long operation. Selection of the two algorithms in practice still needs to consult the practical situation of the study area and contrast their merit and demerit.