A Combined Support Vector Machines Classification Based on Decision Fusion

Decision fusion for classification of hyperspectral data from urban area is addressed. Classical classification algorithms are based on the spectral signature of the individual classes. For urban area, where classes could be defined in accordance with the shape of the structure, these methods have a major drawback: no spatial information are contained in the spectrum. A new method has been proposed that considers the spatial content, but it reduces the spectrum to a small number of bands and does not exploit the spectral richness of the hyperspectral data. In this paper, we propose to use both approaches, and then fuse them. The data are first preprocessed to extract some spatial information. Using Support Vector Machines (SVMs), the data are classified. Finally, according to the property of SVMs outputs, we propose to fuse the results using three different operators. Results are presented on real hyperspectral data from urban area. The proposed approach is positively compared to the results obtained by each of the classifiers used separately.