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Study on classification methods of remote sensing image based on decision tree technology
Shen Wenming(申文民); Wu Guozeng; Sun Zhongping; Xiong Wencheng; Fu Zhuo; Xiao Rulin
Source Publication2011 International Conference on Computer Science and Service System, CSSS 2011 - Proceedings ; 2011 International Conference on Computer Science and Service System, CSSS 2011 ; 2011 International Conference on Computer Science and Service System, CSSS 2011 - Proceedings ; 2011 International Conference on Computer Science and Service System, CSSS 2011
2011
Source Publication2011 International Conference on Computer Science and Service System, CSSS 2011 - Proceedings ; 2011 International Conference on Computer Science and Service System, CSSS 2011 ; 2011 International Conference on Computer Science and Service System, CSSS 2011 - Proceedings ; 2011 International Conference on Computer Science and Service System, CSSS 2011
Corresponding AuthorShen Wenming(申文民)
Conference DateJune 27, 2011 - June 29, 2011
Conference PlaceNanjing, China
Publication Place445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
PublisherIEEE Computer Society
Funding OrganizationZhejiang University; Nanjing University; Nanjing University of Science and Technology; Shanghai Jiao Tong University; University of Science and Technology of China
AbstractIn order to improve and enforce environmental monitoring ability, especially in fields of large scale monitoring and dynamic monitoring, the Environmental Satellite will be launched in 2008 in China. Before the Satellite is launched, necessary pre-research work has to be done. Considering future ecological monitoring demand, we have paid more attention to land use/land cover classification method based on the Satellite's CCD sensor. In this article, we compared the decision tree classification technology with other classic automatic classification technologies using Landsat ETM+ image data and GIS data of Tangshan City in Hebei, China. The result of this study showed: accuracy of decision tree classification compared with the classic automatic classification technologies was improved by 18.29%, Kappa coefficient was increased about 0.1878; classification accuracy was improved about 19.52% when DEM and its derivative data were used as ancillary data in the mountainous area, Kappa coefficient was increased about 0.281; the classification accuracy was improved by 15.86% when the DN(Digital Number) values were converted to at-satellite reflectance values; tasseled cap transformation could cause classification accuracy to be reduced appreciably accompanied by compression of data amount.
KeywordComputer Science Data Compression Decision Trees Engineering Research Image Reconstruction Measurement Theory Metadata Plant Extracts Reflection Remote Sensing Satellites Technology
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/6550
Collection研究生部
Corresponding AuthorShen Wenming(申文民)
Recommended Citation
GB/T 7714
Shen Wenming,Wu Guozeng,Sun Zhongping,et al. Study on classification methods of remote sensing image based on decision tree technology[C]. 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States:IEEE Computer Society,2011.
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