Method of Salt-affected Soil Information Eztraction Based on A Support Vector Machine with Tezture Features
This paper proposed an effort to apply the improved support vector machine classifier to classify the salt-affected soil. In this study, we used the support vector machine with texture features to extract the thematic information of 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 of the Yinchuan plain. Specifically, the accuracy of this method was 84.6974％ and the kappa coefficient was 0.8202, which was superior to the other classification methods.
Support Vector Machine Tezture Analysis Salt-affected Soil CBERS images
Cai Simin Zhang Rongqun Liu Liming Zhou De
Department of Information and Electrical Engineering,China Agricultural University,Beijing,China Department of Resources and Environment,China Agricultural University,Beijing,China
国际会议
北京
英文
1-8
2009-10-14（万方平台首次上网日期，不代表论文的发表时间）