Object-based Land Cover Classification of Urban Areas Using Vhr Imagery and Photogrammetrically-derived Dsm

Object-based image analysis is becoming increasingly popular in classification of very high resolution (VHR) imagery over urban areas. The spectral resolution of VHR imagery (generally they possesses 1 pan and 4 multispectral bands), however, is limited and insufficient for differentiating many urban land cover classes. Due to the spectral similarity of building roofs, roads and parking lots, spectral-based classifications which solely rely on spectral information of the image do not have promising results when applied to VHR imagery over urban landscapes. In recent years, significant amount of research has been carried out on incorporating LiDAR derived DSM into the classification to address the problems of differentiating spectrally similar objects in urban areas. However, LiDAR DSMs are expensive and not available for many urban areas. In this research, we introduce a new approach for classifying urban land cover classes by incorporating widely available photogrammetrically-derived DSMs. Even though the accuracy of photogrammetrically-derived DSMs is far below that of LiDAR DSMs, and significant misregistration exists between VHR imagery and DSM, objectbased hierarchical fuzzy classification still achieve successful separation between building roofs and traffic areas. Stereo aerial photos and a pansharped QuickBird multispectral image of the downtown area of the city of Fredericton, Canada, were used for this research. Results show that buildings can be well separated from roads and parking lots, and the proposed approach has the potential to replace LiDAR DSM for urban land cover classification.