Hierarchically exploring the width of spectral bands for urban material classification

In urban areas, material maps, i.e. knowledge concerning the roofing materials or the different kinds of ground areas, are necessary for several city modeling or monitoring applications. Airborne remote sensing techniques appear to be convenient for providing them at a large scale but require an enhanced imagery spectral resolution. A superspectral sensor with a limited number of bands dedicated to urban materials classification could be a solution. Within this context, this study focused on the optimization of this band subset from hyperspectral data, considering both the position of the bands and their width. The used approach first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Several band configurations are thus explored within this hierarchy. This method was applied to a data set consisting of spectra generated from reflectance spectral signatures of 9 common urban materials collected from 7 spectral libraries. At the end, the potential of a superspectral sensor with wider bands was confirmed.