Fusion of hyperspectral and LiDAR data in classification of urban areas

In this paper, the fusion of hyperspectral and Li-DAR 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 hyper-spectral 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.