Application of a new remote sensing image interpretation method in karst area - support vector machine algorithm
-
摘要: 现行的遥感影像解译方法有监督分类和非监督分类。在监督分类中有平行算法,最小距离算法、最大似然算法等,而支持向量机是监督分类中的一种新的算法。本研究选择贵阳市花溪区小碧乡局部地区为研究对象,采用SPOT数据,分别运用最大似然算法和支持向量机算法对研究区遥感影像进行解译。通过建立混淆矩阵,来计算分类精度和Kappa系数。结果表明:支持向量机具有分类精度高,分类图斑完整等优点;但在时间的消耗上,支持向量机算法要比最大似然算法长。对于这两种算法而言,都存在地物光谱特征明显相异的地物易于区别,光谱相似的地物容易造成错分的现象,然而支持向量机分类精度要比最大似然分类精度高一些。支持向量机对样本数量具有敏感性,样本数量过多将导致运算时间过长。因此在实际运用中应根据实际情况,选择适合的算法。Abstract: 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.
-
[1] 惠文华.基于支持向量机的遥感图像分类方法[J].地球科学与环境学,2006,28(2):93—95. [2] 贵阳市花溪区人民政府网. [3] 《花溪区综合农业区划》编写组.花溪区综合农业区划[M].贵阳:贵州人民出版社,1989:53-150. [4] 赵英时等,遥感应用分析原理与方法[M].北京:科学出版社,2003. [5] 沈焕锋,钟燕飞等,ENVI遥感影像处理方法[M].武昌:武汉大学出版社,2009. [6] 温兴平,胡光道,杨晓峰.基于支持向量机的CBERS-02卫星影像信息提取[J].测绘科学,2008,33(5):146—148. [7] Keerthi S S, Lin C J. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel [J]. Neural Computation,2003,15(7):1667-1689. [8] Lin H T, Lin C J. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods,Technical report[R].2003, Department of Computer Science, National Taiwan University. [9] 喻琴.基于决策树模型的喀斯特石漠化光谱信息自动提取研究[D].贵州师范大学硕士学位论文,贵阳,2009. -

计量
- 文章访问数: 2772
- HTML浏览量: 393
- PDF下载量: 2427
- 被引次数: 0