Qi Bin, Zhao Chunhui, Youn Eunseog, Nansen Christian
College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China.
Opt Express. 2011 Dec 19;19(27):26816-26. doi: 10.1364/OE.19.026816.
Support vector machine (SVM) is widely used in classification of hyperspectral reflectance data. In traditional SVM, features are generated from all or subsets of spectral bands with each feature contributing equally to the classification. In classification of small hyperspectral reflectance data sets, a common challenge is Hughes phenomenon, which is caused by many redundant features and resulting in subsequent poor classification accuracy. In this study, we examined two approaches to assigning weights to SVM features to increase classification accuracy and reduce adverse effects of Hughes phenomenon: 1) "RSVM" refers to support vector machine with relief feature weighting algorithm, and 2) "FRSVM" refers to support vector machine with fuzzy relief feature weighting algorithm. We used standardized weights to extract a subset of features with high classification contribution. Analyses were conducted on a reflectance data set of individual corn kernels from three inbred lines and a public data set with three selected land-cover classes. Both weighting methods and reduction of features increased classification accuracy of traditional SVM and therefore reduced adverse effects of Hughes phenomenon.
支持向量机（SVM）广泛应用于高光谱反射率数据的分类。在传统的支持向量机中，特征是从所有光谱波段或其子集生成的，每个特征对分类的贡献相同。在小型高光谱反射率数据集的分类中，一个常见的挑战是休斯现象，它由许多冗余特征引起，导致随后的分类精度较差。在本研究中，我们研究了两种为支持向量机特征分配权重的方法，以提高分类精度并减少休斯现象的不利影响：1）“RSVM”指具有 Relief 特征加权算法的支持向量机，2）“FRSVM”指具有模糊 Relief 特征加权算法的支持向量机。我们使用标准化权重来提取具有高分类贡献的特征子集。对来自三个自交系的单个玉米粒的反射率数据集以及具有三个选定土地覆盖类别的公共数据集进行了分析。两种加权方法和特征约简都提高了传统支持向量机的分类精度，因此减少了休斯现象的不利影响。
