期刊文献+

基于支持向量机遥感图像融合分类方法研究进展 被引量:5

Research Advances in Remote Sensing Image Fusion and Classification Using Support Vector Machine
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摘要 总结了近年来国际上图像融合以及支持向量机的应用的研究进展,分析了支持向量机用于遥感图像融合分类的趋势、优势以及目前存在的问题,指出了该研究领域的研究方向。 Firstly,research advances of remote sensing image fusion and classification and the application of support vector machine were reviewed.Meanwhile,the tendency,advantages and problems of remote sensing image fusion and classification using support vector machine were analyzed.Finally,the future research direction was pointed out.
出处 《安徽农业科学》 CAS 北大核心 2010年第17期9235-9238,共4页 Journal of Anhui Agricultural Sciences
基金 国家自然科学基金资助项目(40801124) 中国科学院知识创新工程资助项目(kzcx2-yw-224) 中国科学院信息化专项项目(INFO-115-C01-SDB4-17)
关键词 遥感图像 信息提取 融合分类 支持向量机 Remote sensing image Information abstraction Fusion and classification Support vector machine
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参考文献27

  • 1POHL C.Multisensor image fusion in remote sensing:concepts,methods and applications[J].Int J Remote Sensing,1998,19(5):823-854.
  • 2刘继琳,李军.多源遥感影像融合[J].遥感学报,1998,2(1):47-50. 被引量:109
  • 3ZHANG Z,BLUM R S.A Hybrid Image Registration Technique for A Digital Camera Image Fusion Application[J].Information Fusion,2001,2:135-149.
  • 4李峰,周源华.基于小波变换的图像匹配算法[J].上海交通大学学报,1999,33(9):1161-1163. 被引量:15
  • 5武锋强,臧德彦,王建强.数字图像融合研究现状及其评述[J].水利科技与经济,2007,13(1):49-51. 被引量:6
  • 6BOSER B E,GUYON I M,VAPNIK V A.Training Algorithm for Optimal Margin Classifiers[M].New York:ACM Press,1992:144-152.
  • 7VAPNIK V.Nature of Statistical Learning Theory[M].New York:John Wiley and Sons Inc.,1995.
  • 8CORTES C,VAPNIK V.Support Vector Networks[J].Machine Learning,1995,20(3):273-297.
  • 9OSUNA E,FREUND R,GIROSI F.An Improved Training Algorithm for Support Vector Machines[M].New York:IEEE Press,1997:276-285.
  • 10KEERTHI S,GILBERT E.Convergence of a generalized SMO algorithm for SVM classifier design[J].Machine Learning,2002,46(1/3):351-360.

二级参考文献34

  • 1张祖勋 张剑清.数字摄影测量学[M].武汉:武汉测绘科技大学出版社,1993..
  • 2[6]Tsoa B,Olsen R C.A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process[J].Remote Sensing of Environment,2005,97:127-136.
  • 3[7]Wan W,Fraser D.Multisource Data Fusion with Multiple Self-organizing Maps[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37:1344-1349.
  • 4[8]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.
  • 5[9]Zhu G,Blumberg D G.Classification Using ASTER Data and SVM Algorithms; The Case Study of Beer Sheva,Israel[J].Remote Sensing of Environment,2002,80:233-240.
  • 6[10]Benediktsson J A,Swain P H.Hybrid Consensus Theoretic Classification[J].IEEE Transactions on Geoscience & Remote Sensing,1997,35:833-843.
  • 7[1]Welch R,Ehlers M.Merging Multiresolution SPOT HRV and Landsat TM Data[J].Photogrammetric Engineering & Remote Sensing,1987,53:301-303.
  • 8[2]Carper W J,Lillesand T M,Kiefer R W.The Use of Intensity Hue Saturation Transformations for Merging SPOT Panchromatic and Multispectral Images[J].Photogrammetric Engineering & Remote Sensing,1990,56:459-467.
  • 9[3]Ranchin T,Wald L.The Wavelet Transform for the Analysis of Remotely Sensed Images[J].International Journal of Remote Sensing,1993,14:615-619.
  • 10[4]Petrakos M,Benediktsson J A,Kanellopoulos I.The Effect of Classifier Agreement on the Accuracy of the Combined Classifier in Decision Level Fusion[J].IEEE Transactions on Geoscience & Remote Sensing,2001,39:2539-2546.

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