Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. 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. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.

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The classification of urban data with high spectral and spatial resolution is considered. For processing, a morphological profile is constructed. The morphological profile is based on the repeated use of opening and closings with a differently sized structuring element. Morphological profiles have been shown to contain redundancies. Therefore, feature extraction is applied on the profile. The morphological approach is applied in experiments on high resolution DAIS remote sensing data from an urban area. To apply the morphological approach on the DAIS data, the first principal component is used as a basis for the morphological transformations.In experiments, the use of the morphological method performs well in terms of classification accuracies. With feature extraction, it is observed that classification on reduced features gives higher accuracies than in the original feature space.

International audienceClassification of hyperspectral data with high spatial resolution from urban areas is discussed. A previously proposed approach is based on using several principal components from the hyperspectral data to 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 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 applying data fusion on the original data and the morphological information, after feature extraction. The proposed approach is tested in experiments on two different high resolution remote sensing data sets from urban areas