Including the spatial context into decision fusion for urban area mapping using hyperspectral data

In this paper we propose two novel methodologies to incorporate spatial information into ensemble classification systems to process hyperspectral data acquired over urban environment. We introduce the methodologies for extending Hierarchical Binary Decision Tree Classification structure based ensemble (HBDTC) and Class probability Membership value based Ensemble (PMVE) structures with capability to use information from the spatial domain while optimizing the classification structure. In current study a Canny edge detector based clustering and region growing based image segmentation are combined to obtain image object features and after optimizing the ensemble structures in the spectral domain a further optimization is carried out using the identified image objects and refinement in the labelling is done. The obtained classification results show great potential to use spectral-spatial ensemble classification structures for generic mapping of the urban environment. In the paper we demonstrate on two different scenes that both HBDTC spatial algorithm and PMVE spatial algorithms outperform ensemble classification without spatial extension, even if coupled with spatial post.