山东省寿光市滨海地区盐田信息提取方法研究
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摘要
山东省寿光市滨海地区盐田水体因含盐度高,其光谱特征与海域水体及其他地物差异大,光谱特征显著;盐田系人为建造,排列整齐、几何特征明显,遥感影像上表现为纹理特征显著(棋盘状纹理、条纹状纹理),纹理指标可计算性强。首先采用缨帽变换方法增强光谱信息,采用定向滤波及灰度共生矩阵方法增强纹理信息;其次基于增强的光谱与纹理信息,采用以面向应用为目的的感兴趣地物提取方法对研究区TM图像进行分类,将分类结果与仅依据纯光谱及仅依据纯纹理分类结果相对比,分类总体精度分别为90.8985%、84.9102%和60.4017%。结果表明:以面向应用为目的的感兴趣地物提取方法分类精度最高。
Because of the high salinity,the spectral features of the salt field in the coastal areas in Shouguang of Shandong are greatly different from marine water bodies and other ground objects spectral features;the salt field is man-made,so,it has regular arrangement and significant geometric characteristics.In the remote sensing images,the above characteristics are shown as significant texture features(chessboard-like texture and stripe-like texture)and the texture indices have an excellent computability.In this paper,Firstly,using the tasseled cap transform to enhance spectral features of the salt field and using the directional convolution and the gray level co-occurrence matrix to enhance the texture features of the salt field.Secondly,based on the enhanced spectral and texture features,the TM image of the study area is classified by using the application-oriented interested ground objects extraction.The classification result is compared with the classification results based on spectrum only or texture only,the overall accuracy is respectively 90.8985%,84.9102%and 60.4017%.The result indicates that the accuracy of the classification method proposed in this paper is the highest.
Because of the high salinity,the spectral features of the salt field in the coastal areas in Shouguang of Shandong are greatly different from marine water bodies and other ground objects spectral features;the salt field is man-made,so,it has regular arrangement and significant geometric characteristics.In the remote sensing images,the above characteristics are shown as significant texture features(chessboard-like texture and stripe-like texture)and the texture indices have an excellent computability.In this paper,Firstly,using the tasseled cap transform to enhance spectral features of the salt field and using the directional convolution and the gray level co-occurrence matrix to enhance the texture features of the salt field.Secondly,based on the enhanced spectral and texture features,the TM image of the study area is classified by using the application-oriented interested ground objects extraction.The classification result is compared with the classification results based on spectrum only or texture only,the overall accuracy is respectively 90.8985%,84.9102%and 60.4017%.The result indicates that the accuracy of the classification method proposed in this paper is the highest.
引文
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[12]Dixon B,Candade N.Multispectral Landuse Classification U-sing Neural Networks and Support Vector Machines:One orthe Other,or Both?[J].International Journal of RemoteSensing,2008,29(4):1185-1206.
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[16]Pal M,Mather P M.Support Vector Machines for Classifica-tion in Remote Sensing[J].International Journal of RemoteSensing,2005,3(26):1007-1011.
[2]Peng Wanglu,Bai Zhenping,Liu Xiangnan,et al.Introductionto Remote Sensing[M].Beijing:Higher Education Press,2002.[彭望琭,白振平,刘湘南,等.遥感概论[M].北京:高等教育出版社,2002.]
[3]Chica-Olmo M,Arbarca-Hernandez F.Computing Geostatisti-cal Image Texture for Remotely Sensed Data Classification[J].Computers&Geosciences,2000,26(4):373-383.
[4]Xu Jiandong,Luan Peng,Fan Xiaoying,et al.Analysis ofSpectrum and Texture Information on Changbaishan TianchiVolcano Caldera and Its Application[J].Seismology and Geol-ogy,2009,31(4):607-616.[许建东,栾鹏,樊笑英,等.基于遥感影像光谱与纹理分析的地物分类——以长白山天池火山地区为例[J].地震地质,2009,31(4):607-616.]
[5]Ren Xianyi,Zhang Jialin,Chen Chaoyang,et al.SegmentingTextureImages Using Texture Spectrum Method[J].Journalof Image and Graphics,1998,3(12):983-986.[任仙怡,张佳林,陈朝阳,等.基于纹理谱的纹理分割方法[J].中国图象图形学报,1998,3(12):983-986.]
[6]Shu Ning.Remote Sensing Image Texture Analysis and Frac-tal Assessment[J].Journal of Wuhan Technical University ofSurveying and Mapping,1998,23(4):370-373.[舒宁.卫星遥感影像纹理分析与分形分维方法[J].武汉测绘科技大学学报,1998,23(4):370-373.]
[7]Jiang Qingxiang,Liu Huiping.Extracting TM Image Informa-tion Using Texture Analysis[J].Journal of Remote Sensing,2004,8(5):458-464.[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.]
[8]Wang Huanping,Liu Yong.The Study on Classification ofHigh Resolution Remote Sensing Image in Saline Area basedon Window Fourier Transform Power Spectrum Analysis[J].Remote Sensing Technology and Application,2011,26(2):233-238.[王焕萍,刘勇.基于窗口傅立叶变换功率分析的盐田地区高分辨率遥感影像分割分类方法探讨[J].遥感技术与应用,2011,26(2):233-238.]
[9]Gao Chengcheng,Hui Xiaowei.GLCM-based Texture FeatureExtraction[J].Computer Systems&Applications,2010,19(6):195-198.[高程程,惠晓威.基于灰度共生矩阵的纹理特征提取[J].计算机系统应用,2010,19(6):195-198.]
[10]Haralick R M,Shanmugam K,Dinstein I.Textural Featuresfor Image Classification[J].IEEE Transactions on Systems,Man and Cybernetics,1973,3(6):610-621.
[11]Cui Bingde.Remote Sensing Image Classification based onSVM Classifier[J].Computer Engineering and Applications,2011,47(27):189-191.[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.]
[12]Dixon B,Candade N.Multispectral Landuse Classification U-sing Neural Networks and Support Vector Machines:One orthe Other,or Both?[J].International Journal of RemoteSensing,2008,29(4):1185-1206.
[13]Li Panchi,Xu Shaohua.Support Vector Machine and KernelFunction Characteristic Analysis in Pattern Recognition[J].Computer Engineering and Design,2005,26(2):302-304.[李盼池,许少华.支持向量机在模式识别中的核函数特性分析[J].计算机工程与设计,2005,26(2):302-304.]
[14]Farid M,Lorenzo B.Classification of Hyperspectral RemoteSensing Images with Support Vector Machines[J].IEEETransactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[15]Foody G M,Mather A.A Relative Evaluation of MulticlassImage Classification by Support Vector Machines[J].IEEETransactions on Geoscience and Remote Sensing,2004,6(42):1335-1343.
[16]Pal M,Mather P M.Support Vector Machines for Classifica-tion in Remote Sensing[J].International Journal of RemoteSensing,2005,3(26):1007-1011.
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