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基于支持向量机的高光谱遥感图像分类

Keywords: 高光谱遥感,支持向量机,分类

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Abstract:

多数传统分类算法应用于高光谱分类都存在运算速度慢、精度比较低和难以收敛等问题.本文从支持向量机基本理论出发建立了一个基于支持向量机的高光谱分类器,并用国产OMIS传感器获得的北京中关村地区高光谱遥感数据进行试验,分析比较了各种SVM核函数进行高光谱分类的精度,以及网格搜寻的方法来确定C和愕闹?结果表明SVM进行高光谱分类时候径向基核函数的分类精度最高,是分类的首选.并且与神经网络径向基分类算法以及常用的最小距离分类算法进行比较,分类的精度远远高于SVM分类算法进行分类的结果.SVM方法在高光谱遥感分类领域能得到广泛的应用.

References

[1]  【8】Kre el U.Pairwise Classification and Support Vector Machines[M].In Advances in Kernel Methods:Support Vector Learning,B.Scholkopf,C.J.C.Burges,and A.J.Smola (eds.),The MIT Press,Cambridge,MA,1999:255-268.
[2]  【9】Hsu C W,Lin C J.A comparison of methods for multi-class support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):415-425,2002.
[3]  【10】Boardman J W,Kruse F A.Automated spectral analysis:a geological example using AVIRIS data,north Grapevine Mountains[C].Nevada:In Proceeding,ERIM Tenth Thematic Conference on Geologic Remote Sensing,Environmental Research Institute of Michigan,Ann Arbor,MI,1994,I-407-I-418.
[4]  【1】程熙,沈占锋,骆剑承,沈金祥,胡晓东,朱长明, "METHOD ON SIMULATING REMOTE SENSING IMAGE BAND BY USING GROUND-OBJECT SPECTRAL FEATURES STUDY",红外与毫米波学报 29, 45-48(2010)
[5]  【2】许学斌,张德运,张新曼,曹仰杰, "基于离散曲波变换和支持向量机的掌纹识别方法",红外与毫米波学报 28, 456-460(2009)
[6]  【3】王雷,乔晓艳,董有尔,张姝,尚艳飞, "高光谱图像技术在农产品检测中的应用进展",应用光学 30, 639-645(2009)
[7]  【4】郭 雷,肖怀铁,付 强, "非均衡数据目标识别中SVM模型多参数优化选择方法",红外与毫米波学报 28, 141-145(2009)
[8]  【1】Lothar Hermes,Dieter Frieau,Jan Puzicha,et al.Support Vector Machines for Land Usage Classification in Landsat TM Imagery[J].In:Proc.IGARSS\'99,1:348-350.
[9]  【2】Huang C,Davis L S.Townshend J R G.An assessment of support vector machines for land cover classification[J].International Journal of Remote Sensing,2002,23:725-749.
[10]  【3】Martin Brown,Hugh G Lewis,Steve R Gunn.Linear Spectral Mixture Models and Support Vector Machines for Remote Sensing[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(5):2346-2360.
[11]  DONG Guang-Jun,ZHANG Yong-Sheng,FAN Yong-Hong.Image fusion for hyperspectral data of PHI and high-resolution aerial image[J].J.Infrared Millim.Waves(董广军,张永生,范永弘.基于多特征多分辨率融合的高光谱图像分类.红外与毫米波学报),2006,25(2):123-126.
[12]  【5】Zhang J,Zhang Y,Zhou T.Classification of hyperspectral data using support vector machine[C].In:IEEE International Conference on Image Processing,2001:882-885.
[13]  【6】ZHANG Xue-Gong.Introduction statistical learning theory and support vector machines[J].Act Automatica Sinica(张学工.关于统计学习理论与支持向量机.自动化学报),2000,1(26):32-42.
[14]  【7】Vapnik V N.Statistical Learning Theory[M].New York:Wiley.1998.

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