计算机与现代化Issue(2)：11-14,20,5.DOI:10.3969/j.issn.1006-2475.2016.02.003
高光谱图像数据的多尺度多核SVM分类
Multi-scale Multiple Kernel SVM Classification for Hyperspectral Imagery Data
摘要
Abstract
Aiming at support vector machine ( SVM) using single kernel learning not handling the classification problem of hyper-spectral imagery data that the sample distribution is irregular and complex , a hyperspectral imagery data classification method based on sampling strategy and multiple kernel support vector machine is proposed in this paper .Firstly the method does sampling referring to the minority support vectors (SVs) rather than the training data to provide a balanced distribution during multiple ker -nel support vector machine mode , and then uses the weighted sum approach to multiple kernel learning ( MKL) and optimizes pa-rameters by gradient descent algorithm .Finally, a series of two-class classifiers are used to achieve the multi-class classification. Experimental results show that overall classification accuracy increased by 4.07%, average classification accuracy increased by 9.62%compared with the traditional SVM .关键词
高光谱图像/不平衡分类/多核SVM/过采样/梯度下降算法Key words
hyperspectral image/imbalanced classification/multiple kernel SVM/over-sampling/gradient descent algorithm分类
信息技术与安全科学引用本文复制引用
晁拴社,楚恒,王兴..高光谱图像数据的多尺度多核SVM分类[J].计算机与现代化,2016,(2):11-14,20,5.基金项目
重庆博士后科研项目(Rc201336) （Rc201336）
