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

Abstract

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 can be used all together in one extended morphological profile. 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. Similarly, a pixel-wise classification solely based on the spectral content can be performed, but it lacks information on the structure of the features in the image. An extension is proposed in this paper in order to overcome these dual problems. The proposed method is based on the data fusion of the morphological information and the original hyperspectral data: the two vectors of attributes are concatenated. After a reduction of the dimensionality using Decision Boundary Feature Extraction, 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 of approaches based on the use of morphological profiles based on PCs only and conventional spectral classification.