26 July 2007 Object-oriented classification of very high-resolution remote sensing imagery based on improved CSC and SVM
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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.
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Haitao Li, Haitao Li, Haiyan Gu, Haiyan Gu, Yanshun Han, Yanshun Han, Jinghui Yang, Jinghui Yang, } "Object-oriented classification of very high-resolution remote sensing imagery based on improved CSC and SVM", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67523I (26 July 2007); doi: 10.1117/12.761237; https://doi.org/10.1117/12.761237
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