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Urban mapping techniques using high spectral and spatial resolution (HSSR) data were investigated. To this aim, this paper proposes a novel mean shift (MS)-based multiscale method, and different spatial approaches are compared, including differential morphological profiles (DMPs), pixel shape index (PSI), the fractal net evolution approach (FNEA), and the proposed MS method. These spatial features were computed based on a dimensionally reduced representation that was obtained using the non-negative matrix factorization (NMF) transform. The support vector machine (SVM) was then used for classification. These algorithms were evaluated using two HSSR datasets that were obtained by using the Reflective Optics System Imaging Spectrometer (ROSIS) sensor over the urban area of Pavia, northern Italy. The results show that the spatial approaches can effectively complement the spectral features for urban mapping, and the proposed MS-based multiscale algorithm can give comparable or even better results than the FNEA, DMPs and other traditional algorithms. [ABSTRACT FROM AUTHOR]