Fusion of Morphological and Spectral Information for Classification of Hyperspectal Urban Remote Sensing Data
Classification of hyperspectral data with high spatial resolution from urban areas is discussed. An approach has been proposed which is based on using several principal components from the hyperspectral data and build morphological profiles. These profiles are used all together in one extended morphological profile, which is then classified by a neural network. A shortcoming of the approach is that it is primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. An extension is proposed in this paper in order to overcome the problems with the extended morphological profile approach. The proposed method is based on the data fusion of the morphological information and the original data. The proposed approach is tested in experiments on two different high resolution remote sensing data sets from urban areas. The results are excellent and significant improvements are achieved in terms of accuracies when compared to results of approaches based on the use of morphological profiles based on PCs only and conventional statistical approaches.