Morphological pre-processing for classification of hyperspectral data from urban areas

Classification of hyperspectral data with high spatial resolution is discussed. A method based on mathematical morphology for pre-processing of the hyperspectral data is investigated. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. Then, a morphological profile is constructed based on the repeated use of openings and closings with a differently sized structuring element. In order to apply the morphological approach to hyperspectral data, principal components are computed. Then, the principal components are used as base images for the morphological profiles. The use of extended morphological profiles, based on more than one principal component is proposed. In experiments, two data sets are classified. The proposed method performs well in terms of classification accuracies. It gives similar overall accuracies to statistical approaches.