摘要
[目的]寻找喀斯特地区土地最优分类方法。[方法]选取覆盖柳州市的美国陆地卫星的Landsat-5TM数字影像(2011年),采用最大似然、神经网络和支持向量机(SVM)3种分类方法,对研究区域的土地进行分类,比较分类后的混淆矩阵,分别求出3种分类结果的总体正确率和Kappa系数。[结果]3种分类方法的总体正确率都在90%以上,Kappa系数也较高;SVM分类方法的总体分类正确率和Kappa系数最高,优于神经网络、最大似然法分类。[结论]SVM分类方法可提高喀斯特地区土地利用信息遥感分类的精度,为后期有效地动态监测喀斯特地区土地利用的变化奠定了基础。
[Objective] The aim was to find out the optimal method of land classification in Karst area. [Method] Based on American Landsat 5 （2011） digital images covering Liuzhou City, the maximum likelihood, neural network and support vector machine （SVM） classification methods were studied. Three methods were used to classify the land, the confusion matrix was compared after classification, overall accuracy and Kappa coefficient of classification results were calculated. [Result] The overall accuracy of three classification methods was over 90% , the Kappa coefficient was higher. The overall classification accuracy and the Kappa coefficient of SVM classification method were all the high- est, better than the neural network and maximum likelihood. [ Conclusion ] The SVM method can improve the accuracy of land classification in Karst area, and it is effectively to dynamic ally monitor the change of land used in karst area.
作者
何朝霞
HE Zhao-xia(College of Technology ＆ Engineering, Yangtze University, Jingzhou, Hubei 43402)
出处
《安徽农业科学》
CAS
2017年第1期4-7,共4页
Journal of Anhui Agricultural Sciences
基金
湖北省教育厅2016年度科学研究计划指导性项目(B2016443)
关键词
最大似然
神经网络
支持向量机
土地分类
精度
Maximum likelihood
Neural network
Support vector machine
Land classification
Accuracy
