Over the past two decades, hyperspectral remote sensing from airborne and satellite systems has been used as a data source for numerous applications. Hyperspectral imaging is quickly moving into the mainstream of remote sensing and is being applied to remote sensing research studies. Hyperspectral remote sensing has great potential for analysing complex urban scenes. However, operational applications within urban environments are still limited, despite several studies that have explored the capabilities of hyperspectral data to map urban areas. In this paper, we review the methods for urban classification using hyperspectral remote sensing data and their applications. The general trends indicate that combined spatial-spectral and sensor fusion approaches are the most optimal for hyperspectral urban analysis. It is also clear that urban hyperspectral mapping is currently limited to airborne data, despite the availability of spaceborne hyperspectral systems. Possible future research directions are also discussed.
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