摘要
在支持向量机多类识别基础上探讨以球结构替代传统超平面支持向量机对QuickBird影像进行分类的可行性,对重叠区域的数据分类采用新规则,提高球结构支持向量机算法的泛化性能,并将分类结果与最小距离法、最大似然法分类结果进行比较,实验结果表明该算法有效可行,降低了二次规划的复杂度,缩短了样本训练时间。
After summarization of SVM for multi-class recognition,it analyzes a method of sphere support vector machine which takes place of traditional SVM for QuickBird remote sensing image classification;presents a new classification for intersection data which lesds to a better generalization accuracy and compares it with Minimum Distance classification,maximum likelihood classification methods.Experimental results show the method is feasible,and reduce the complexity of quadratic programming and shorten the training time.
出处
《测绘工程》
CSCD
2011年第3期46-49,共4页
Engineering of Surveying and Mapping
关键词
支持向量机
核函数
超球
多类分类
重叠区数据
support vector machine
kernel function
hypersphere
multi-class classification
intersection data
