Exploiting spectral and spatial information for classifying hyperspectral data in urban areas

This paper is devoted to urban hyperspectral remote sensing. Very high resolution hyperspectral data are used to provide detailed maps of urban land cover, exploiting different classification tools. In particular, multiple classifications and spatial refinement step are used to improve the mapping accuracy. We show results on DAIS data over the town of Pavia, Northern Italy. The four flight lines over the area, kindly provided by DLR in the framework of the HySens project, are partially overlapping. This helps, besides the test of the classification procedure here presented, even to understand the advantages of combining different views of the same area.