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Proceedings Paper

Object-oriented classification of very high-resolution remote sensing imagery based on improved CSC and SVM
Author(s): Haitao Li; Haiyan Gu; Yanshun Han; Jinghui Yang
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Paper Abstract

We present a new object-oriented land cover classification method integrating raster analysis and vector analysis, which adopted improved Color Structure Code (CSC) for segmentation and Support Vector Machine (SVM) for classification using Very High Resolution (VHR) QuickBird data. It synthesized the advantage of digital image processing, Geographical Information System (GIS) (vector-based feature selection) and Data Mining (intelligent SVM classification) to interpret image from pixels to segments and then to thematic information. Compared with the pixelbased SVM classification in ENVI 4.3, both of the accuracy of land cover classification by the proposed method and the computational performance for classification were improved. Moreover, the land cover classification map can update GIS database in a quick and convenient way.

Paper Details

Date Published: 26 July 2007
PDF: 10 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523I (26 July 2007); doi: 10.1117/12.761237
Show Author Affiliations
Haitao Li, Chinese Academy of Surveying and Mapping (China)
Haiyan Gu, Chinese Academy of Surveying and Mapping (China)
Yanshun Han, Chinese Academy of Surveying and Mapping (China)
Jinghui Yang, Chinese Academy of Surveying and Mapping (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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