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14 March 2003 Characterization of urban areas using principal component analysis from multitemporal ERS coherence imagery
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C-band SAR interferometry using ERS data has been shown to be potential for urban areas studies. This work illustrates the application of Principal Components Analysis (PCA) to a multi-temporal set of ERS coherence images to detect urban areas and their features. In particular Principal Component Transformation was applied on sets of one-day and long-term coherence images for urban mapping applications in the area of Naples, Italy. Two main classes, urban and non-urban, which then included two classes each, were considered in this study. Dense built-up areas and residential areas formed the urban class. Water bodies and vegetated areas (fields and woods) were grouped in the non-urban class. The first principal component was found to be more suitable than higher order components for detection of urban areas. Moreover, a simple algorithm based on distance between the first principal component of a pixel and the value representative for each class was tested for intra-urban mapping. Results showed that the first principal component could discriminate reasonably well between dense built-up and residential areas.
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Attilio Fanelli and Maurizio Santoro "Characterization of urban areas using principal component analysis from multitemporal ERS coherence imagery", Proc. SPIE 4886, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, (14 March 2003);

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