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Remote Sensing Technology and Application  2008, Vol. 23 Issue (5): 571-575    DOI: 10.11873/j.issn.1004-0323.2008.5.571
    
Application of Semivariogram Texture Distillingfor Remote Sensing Image Classification
HE Yu-ting,KE Chang-qing
(Department of Geography Information Science,Nanjing University,Nanjing 210089,China)
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Abstract  

In the methods of improving the classification precision of remote sensing images,adding textural information as an expanded eigenvector into feature space is a pretty useful method.In this paper,the author extract texture using spatial connections between geo-objects,then put it into the classification process.This experiment shows a nice result.Through the problems encountered in this experiment,we discussed the appropriate scope of this methodology.

Key words:  Semivariogram      Texture      Scale      Feature space      Classification precision     
Received:  13 March 2008      Published:  07 November 2011
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Cite this article: 

HE Yu-ting,KE Chang-qing. Application of Semivariogram Texture Distillingfor Remote Sensing Image Classification. Remote Sensing Technology and Application, 2008, 23(5): 571-575.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2008.5.571     OR     http://www.rsta.ac.cn/EN/Y2008/V23/I5/571

[1] Tang G A,Zhang Y S,Liu Y M,et al.Digital Remote Sensed Image Processing[M].Beijing:Science Press.2004.[汤国安,张友顺,刘咏梅,等.遥感数字图像处理[M].北京:科学出版社.2004.]
[2] Chica-Olmo M,Abarca-Hernandez F.Computing Geostatistical Image Texture for Remotely Sensed Data Classification[J].Computers & Geosciences,2000,(26):373-383.
[3] Hu W Y,Jiao Y M.The Study Progress of the Texture Feature Extraction for Remote Sensing Image[J].Study on Geographic Environment of YunNan,2007,19(3):66-71.[胡文英,角媛梅.遥感图像纹理信息提取方法综述[J].云南地理环境研究,2007,19(3):66-71.]
[4] Song C Y,Li P J,Yang F J.The Application of MultiScale Image Texture to the Detection of Urban Expansion[J].Remote Sensing for Land & Resources,2006,(3):37-42.[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的检测[J].国土资源遥感,2006,(3):37-42.]
[5] De Miranda F P,Da Fonseca L E N.Application of the Semivariogram Textural Classifier (STC) for Vegetation Discrimination Using JERS-1 SAR Data of the Uaupes River (Northwestern Brazil) [J].Anais VIII Simpósio Brasileiro de Sensoriamento Remote,Salvador,1996,14(19):527-532.
[6]    Andrew T H, Carol A W. Textural Analysis of Historical Aerial Photography to Characterize Woody Plant Encroachment in South African Savanna[J].Remote Sens.Environ.1998,66:317-330.
[7]    Miranda F P.The Semivariogram in Comparison to the Co-occurrence Matrix for Classification of Image Texture [ J].Transaction on Geoscience and Remote Sensing, 1998, 36:1945-1952,317-330.
[8] Lark R M.Geostatistical Description of Texture on an Aerial Photograph for Discriminating Classes of Land Cover [J].International Journal of Remote Sensing,1996,17:2115-2133.
[9] Li P J,Li Z X.Comparison of Three Geostatistical Texture Measures for Remotely Sensed Data Classification [J].Geography and Geo-information Science.2003,19(4):89-92.[李培军,李争晓.三种地统计学图像纹理用于遥感图像分类的比较[J].地理与地理信息科学,2003,19(4):89-92.]
[10]  Huang Y D,Li P J,LiZ X.The Application of Geostatistical Image Texture to Remote Sensing Lithological Classification[J].Remote Sensing for Land & Resources,2003(3):45-49.[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.]
[11] Wang Y F.Methodology of Spatial Data Analyst[M].Beijing:Science Press.2007.[王远飞.空间数据分析方法[M].北京:科学出版社.2007.]
[12] Li X W,Cao C X.The First Law of Geography and SpatialTemporal Proximity[J].Journal of Nature,2006,29(2):69-71.[李小文,曹春香,常超一.地理学第一定律与时空邻近度的提出[J].自然杂志,2006,29(2):69-71.]
[13] 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,1990,284):513-519.

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