FUSION OF HYPERSPECTRAL AND LIDAR DATA IN CLASSIFICATION OF URBAN AREAS

in this paper, the fusion of hyperspectral and LiDAR data is taken into account in order to develop a new classification framework for the accurate analysis of urban areas. In this method, an attribute profile is considered in order to model the spatial information of LiDAR and hyperspectral data. In parallel, in order to reduce the redundancy of the hyperspectral data and address the so-called curse of dimensionality, a supervised feature extraction technique is used. Then, the new features obtained by the attribute profile and the supervised feature extraction technique are concatenated into a stacked vector. The final classification map is achieved by using a Random Forest classifier. Results infer that the proposed method can provide very good results in terms of classification accuracy and CPU processing time in an automatic manner.