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基于纹理特征和支持向量机的ALOS图像土地覆被分类

DOI: 10.6046/gtzyyg.2011.04.11, PP. 58-63

Keywords: 纹理特征,SVM,ALOS,图像,土地覆被,非点源污染

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Abstract:

高空间分辨率遥感图像在土地覆被分类方面应用广泛,但传统的基于像元分类方法的精度较低。为了提高高分辨率图像的分类精度,通过灰度共生矩阵法快速提取纹理特征,利用支持向量机(SVM)并辅以纹理特征,对浙江湖州典型实验样区的ALOS图像进行土地覆被分类。结果表明:基于纹理特征和SVM的图像分类能更好地提取地物信息,分类总精度达到90.88%;单纯SVM的分类精度(89.96%)高于最大似然法(分类精度86.16%)。本文方法可快速准确地提取土地覆被类型,为研究农业非点源污染的产生和时空分布提供服务,进而为寻求太湖流域内合理的土地利用模式和土地的可持续利用提供科学依据。

References

[1]  Zhu G B,Blumberg D G.Classification Using ASTER Data and SVM Algorithms:the Case Study of Beer Sheva,Israel[J].Remote Sensing of Environment,2002,80(2):233-240.
[2]  Keuchel J,Naumann S,Heiler M,et al.Automatic Land Cover Analysis for Tenerife by Supervised Classification Using Remotely Sensed Data[J].Remote Sensing of Environment,2003,86(4):530-541.
[3]  Nemmour H,Chibani Y.Multiple Support Vector Machines for Land Cover Change Detection:an Application for Mapping Urban Extensions[J].ISPRS Journal of Photogrammentry and Remote Sensing,2006,61(2):125-133.
[4]  Inglada J.Automatic Recognition of Man-made Objects in High Resolution Optical Remote Sensing Images by SVM Classification of Geometric Image Features[J].ISPRS Journal of Photogrammetry and Remote Sensing,2007,62(3):236-248.
[5]  刘龙飞,陈云浩,李京.遥感影像纹理分析方法综述与展望[J].遥感技术与应用,2003,18(6):441-447.
[6]  Haralick R M,Shanmugan K,Dinsrein I.Textural Features for Image Classification[J].IEEE Trans actions on Man and Cybern etics,1973,3(6):610-621.
[7]  冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究[J].北京测绘,2007(3):19-22.
[8]  Cristianini N,Shawe-Taylon J.支持向量机导论[M].北京:电子工业出版社,2006.
[9]  张睿,马建文.改进的P-SVM支持向量机与遥感数据分类[J].遥感学报,2009,13(3):445-452.
[10]  张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):37-38.
[11]  张维理,武淑霞,冀宏杰,等.中国农业面源污染形势估计及控制对策Ⅰ--21世纪初期中国农业面源污染的形势估计[J].中国农业科学,2004,37(7):1008-1017.
[12]  姜青香,刘慧平.利用纹理分析方法提取TM图像信息[J].遥感学报,2004,8(5):458-464.
[13]  陈启浩,高伟,刘修国.辅以纹理特征的高分辨率遥感影像分类[J].测绘科学,2008,33(1):88-90.
[14]  Chavez P Jr,Bauer B.An Automatic Optimum Kernel-size Selection Technique for Edge Enhancement[J].Remote Sensing of Environment,1982(1),12:23-38.
[15]  Franklin S E,McDemid G J.Empirical Relations Between Digital SPOT HRV and CASI Spectral Response and Lodgepole Pine (Pinas Contorta) Forest Stand Parameters[J].International Jounal of Remote Sensing,1993,14(12):2331-2348.
[16]  Franklin S E,Wukler M A,Lavingne M B.Automated Derivation of Geographic Window Sizes for Use in Remote Sensing Digital Image Texture Analysis[J].Computers & Geosciences,1996,22(6):665-673.
[17]  Curran P J.The Semivariogram in Remote Sensing:an Introduction[J].Remote Sensing of Environment,1988,24(3):493-507.
[18]  Marceau D J,Howarth P J,Dubois J M,et al.Evaluation of the Grey-level Co-occurrence Matrix Method for Land-cover Classification Using SPOT Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(4):513-519.
[19]  Hsu S.Texture-tone Analysis for Automated Land-use Mapping[J].Photogrammetric Engineering & Remote Sensing,1978,44(11):1393-1404.
[20]  Dutra L V,Mascarenhas N D A.Some Experiments with Spatial Feature Extraction Methods in Multispectral Classification[J].International Journal of Remote Sensing,1984,5(2):303-313.
[21]  黄颖端,李培军,李争晓.基于地统计学的图像纹理在岩性分类中的应用[J].国土资源遥感,2003(3):45-49.
[22]  Hallo,Hay G J,Marceau D J.Multiscale Object-specific Analysis Scale Problems and Multiscale Solutions[C]//Proceedings of 12th International Conference on Geoinfomatics-Geospatial Information Research:Bridging the Pacific and Atlantic.Sweden:Press of University of G?vele,2004.
[23]  宋翠玉,李培军,杨锋杰.运用多尺度图像纹理进行城市扩展变化检测[J].国土资源遥感,2006(3):37-42.

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