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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.

Abstract-A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components from the hyperspectral data and building several morphological profiles. These profiles can be used all together in one extended morphological profile. A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixel-wise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality the final classification is achieved using a Support Vector Machines classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of morphological profiles based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set.Index Terms-Data fusion, hyperspectral data, support vector machines, feature extraction, extended morphological profile, high spatial resolution. I. INTRODUCTIONIn classification of remote sensing data from urban areas, the identification of relatively small objects, e.g., houses and narrow streets is important. Therefore, high spatial resolution of the imagery is necessary for accurate classification. The most commonly available remote sensing data of high spatial resolution from urban areas are single-band panchromatic data. However, using only one high-resolution panchromatic data channel is usually not sufficient for accurate classification of structural information. To overcome that problem, Pesaresi and Benediktsson [1] proposed the use of morphological transformations to build a Morphological Profile (MP). In [2] the method in [1] was extended for hyperspectral data with high spatial resolution. The approach in [2] is based on using several Principal Components (PCs) from the hyperspectral data. From each of the PCs, a morphological profile is built. These profiles are used all together in one Extended Morphological Profile (EMP), which is then classified by a neural network. The method in [2] has been shown to perform well in terms of accuracies when compared to more conventional classification approaches. However, a shortcoming of the approach is that it is pr...

Abstract-A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components from the hyperspectral data and building several morphological profiles. These profiles can be used all together in one extended morphological profile. A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixel-wise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality the final classification is achieved using a Support Vector Machines classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of morphological profiles based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set.Index Terms-Data fusion, hyperspectral data, support vector machines, feature extraction, extended morphological profile, high spatial resolution. I. INTRODUCTIONIn classification of remote sensing data from urban areas, the identification of relatively small objects, e.g., houses and narrow streets is important. Therefore, high spatial resolution of the imagery is necessary for accurate classification. The most commonly available remote sensing data of high spatial resolution from urban areas are single-band panchromatic data. However, using only one high-resolution panchromatic data channel is usually not sufficient for accurate classification of structural information. To overcome that problem, Pesaresi and Benediktsson [1] proposed the use of morphological transformations to build a Morphological Profile (MP). In [2] the method in [1] was extended for hyperspectral data with high spatial resolution. The approach in [2] is based on using several Principal Components (PCs) from the hyperspectral data. From each of the PCs, a morphological profile is built. These profiles are used all together in one Extended Morphological Profile (EMP), which is then classified by a neural network. The method in [2] has been shown to perform well in terms of accuracies when compared to more conventional classification approaches. However, a shortcoming of the approach is that it is pr...

Abstract-The combination of multisource remote sensing and geographic data is believed to offer improved accuracies in land cover classification. For such classification, the conventional parametric statistical classifiers, which have been applied successfully in remote sensing for the last two decades, are not appropriate, since a convenient multivariate statistical model does not exist for the data. In this paper, several single and multiple classifiers, that are appropriate for the classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus-theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for two multisource remote sensing and geographic datasets. In the experiments, the multiple classifiers outperform the single classifiers in terms of overall accuracies.