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The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery

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Two techniques, integrating texture and spatial context properties for the classification of fine spatial resolution imagery from the city of Athens (Hellas) have been tested in terms of accuracy and class specificity. Both techniques were kernel based, using an artificial neural network and the kernel reclassification algorithm. The study demonstrated the high potential of the kernel classifiers to discriminate residential categories on 5 m-spatial resolution imagery. The overall accuracy percentages achieved were 73.44% and 74.22% respectively, considering a seven-class classification scheme. The adopted scheme was subset of the nomenclature referred to as 'Classification for Land Use Statistics Eurostat's Remote Sensing programme' (CLUSTERS) used by the Statistical Office of the European Communities (EUROSTAT) to map urban and rural environment.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160050145027

Affiliations: 1: Institute for Space Applications and Remote Sensing, National Observatory of Athens, Metaxa and Vas.Pavlou Str, Palaia Penteli, GR 15236, Athens, Greece 2: GAFmbH, Company for Applied Remote Sensing, Arnulfstr. 197, 80634, Munich, Germany

Publication date: November 10, 2000

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