Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles

Abstract

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...

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