The nature of statistical learning theory
The nature of statistical learning theory
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.98 |
This paper describes an effort to apply an improved support vector machine classifier to classify salt-affected soil. In this study, we used the support vector machine with texture features to extract thematic information for salt-affected soil. The SVM classification was conducted using a combination of multi-spectral features and texture features as the data source. We used mean, variance and homogeneity features, which were the best texture features, to improve the classification. In addition, we provided a contrast between the proposed SVM method and other SVM methods. The results revealed that the SVM classification used here can effectively extract salinization soil thematic information for the Yinchuan Plain. Specifically, the accuracy of this method was 84.6974% and the kappa coefficient was 0.8202, which indicated superiority over other classification methods.