计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (27): 189-191.
• 图形、图像、模式识别 • 上一篇 下一篇
崔炳德
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CUI Bingde
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摘要： SVM分类器核函数的选择以及参数的设置直接影响系统的泛化能力和运行速度。引入交叉验证技术和栅格搜索技术，对径向基核、多项式核和Sigmoid核函数应用于图像多类别分类的性能进行理论推导、测试及分析，求得三种核函数应用于SVM分类器的性能，并证明了栅格搜索寻找最优参数的有效性。最后通过对TM 6波段BSQ格式遥感图像进行分类对比证明了SVM分类器核函数用于TM图像分类的可行性及高效性。
关键词: 支持向量机算法, 核函数, 图像分类
Abstract: How to choose the kernel function of the SVM classifier and function’s parameters affects system’s generalization and operating speed directly.It takes cross validation and grid search to validate the performance of radial basis kernel，polynomial kernel and sigmoid kernel functions in multi-class classification，which not only deduce the capability in SVM but also prove the effectiveness of grid search in finding optimized characters.Finally，the three SVM classifier kernel functions are used to classify BSQ remote sensing image in TM6 band，the experimental data shows their feasibility and higher efficiency.
Key words: Support Vector Machine（SVM） algorithm, kernel function, image classification
崔炳德. 支持向量机分类器遥感图像分类研究[J]. 计算机工程与应用, 2011, 47(27): 189-191.
CUI Bingde. Remote sensing image classification based on SVM classifier[J]. Computer Engineering and Applications, 2011, 47(27): 189-191.
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