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Title : IMPROVING CLASSIFICATION ACCURACY OF SPECTRALLY SIMILAR URBAN CLASSES BY USING OBJECT-ORIENTED CLASSIFICATION TECHNIQUES: A CASE STUDY OF NEW YORK CITY
Company : Department of Environmental, Geographic and Geological Sciences Lehman College, City University of New York (CUNY)
File Name : Bhaskaran.pdf
Size : 347846
Type : application/pdf
Date : 22-May-2010
Downloads : 8

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Featured Paper by

Sunil Bhaskaran

Paper describes a methodology to improve classification of urban features by using object oriented classification techniques. Mapping urban features from satellite data is challenging due to several reasons. Urban objects are spectrally similar and they have different shapes, sizes, patterns, all of which contribute to their low accuracy. For example, features such as roofs, roads, and other spectrally similar objects like open space (concrete) appear spectrally similar leading to their low accuracy in classification. Very high resolution satellite data (Ikonos) was classified using both supervised and object oriented classification techniques. A combination of spectral, spatial attributes and membership functions were employed for mapping urban features.
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