Assessment of Land Cover Changes Using Taguchi-Based Optimized SVM Classification Approach
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Land use and land cover mapping is of great importance in many research areas. The main objective of this study was to assess land cover changes using support vector machine (SVM) classification approach. The most important challenge in this case is to determine the optimum structure of classification method. The optimization Taguchi method was performed to optimize the structure of SVM. Results showed that Taguchi method can be effectively used to cope with this problem. In fact, using Taguchi method to optimize SVM parameters can significantly decrease the number of classification tests. In this study, land use/cover maps of the Yazd–Ardakan plain were produced in three dates of 1990, 2002 and 2015. Maximum and minimum accuracy of the classification was 0.9 and 0.85 for 2015 and 1990 imagery, respectively. This may be related to the differences in the quality of the imagery. The land use/cover changes were then assessed based on the best classification results. It was shown that maximum change in the land use/cover was taken place in residential areas.
KeywordsChange detection Land cover Taguchi method SVM Landsat Yazd
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