摘要
支持向量机因其适用高维特征、小样本与不确定性问题的优越性,是一种极具潜力的高光谱遥感分类方法.核函数是支持向量机的核心,核函数分为局部核函数与全局核函数两大类,不同的核函数将产生不同的分类效果.核函数也是支持向量机理论中比较难理解的一部分.在基本核函数中引入光谱匹配识别中的典型方法——光谱角度匹配法(SAM法),兼顾到光谱亮度与光谱向量方向的距离测度,结合最小二乘支持向量机,通过与传统SVM分类方法的比较,证明这种方法的有效性.
Support Vector Machines （SVM） is a potential hyperspectral remote sensing classification method because it is advantageous to deal with problems with high dimensions, small samples and uncertainty. Kernel functions are key part of SVM, and they are divided into local and whole types. Different kernels can produce different classification effects. In basic kernel functions, spectral angle matching method-the classical spectral matching meth- od-is introduced and the distance measure is taken into account. SVM can deal with nonlinear problems in classification and regression easily by using kernel functions. In the paper, an SAM （Spectral Angle Mapping）algorithm that is the classical algorithm for spectral matching recognition is introduced. By comparing with Euclid distance,a distance measure based on spectral brightness and spectral vector direction（ close to spectral shape）is presented.
出处
《应用科技》
CAS
2008年第1期44-47,52,共5页
Applied Science and Technology
关键词
最小二乘支持向量机
光谱角
高光谱遥感分类
least square support vector machine
spectral angle matching
hyperspectral remote sensing classification
