In this paper we present a robust and efficient approach for automatic urban scene classification based on imagery and elevation data. Scene classification is of great interest for a broad spectrum of applications, e.g., city models, urban planning and land cover/use. Because of the availability of high resolution imagery and the corresponding scene complexity as well as heterogeneous appearance of objects, scene classification of urban areas is still challenging with respect to accuracy and efficiency. To this end, we propose "relative features", which are intra-class stable and inter-class discriminative, instead of absolute ones for color and geometry to deal with object diversity and scene complexity. The proposed approach provides a pixel-wise as well as a patch-wise schemes with (1) robustness against the variability of object appearance, (2) adaptation to undulating terrain and (3) fully-parallel processing for feature extraction and classification. Experiments on public benchmark and self-acquired data demonstrate the potential of the proposed approach.
['Hai Huang', 'Helmut Mayer']
Robust and efficient urban scene classification using relative features