Land-cover classification using both hyperspectral and LiDAR data

The increased availability of data from different satellite and airborne sensors from a particular scene makes it desirable to jointly use data from multiple data sources for improved information extraction and classification. In particular, hyperspectral sensors provide valuable spectral information that can be used to discriminate different classes of interest, but they do not provide structural and elevation information. On the other hand, LiDAR data can extract useful information related to the size, structure and elevation of different objects, but cannot model the spectral characteristics of different materials. In this paper, a new classification framework is proposed by considering the integration of hyperspectral and LiDAR data. In this case, the recently introduced theoretically sound attribute profile (AP) is considered 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, supervised feature extraction techniques are taken into account. Then, the new features obtained by the AP and the supervised feature extraction techniques are concatenated into a stacked vector. The final classification map is achieved by using either support vector machine or random forest classification techniques. The proposed method was applied on two data sets and the obtained results were compared in terms of classification accuracies and CPU processing time. From the results it can be concluded that the proposed method can classify the integration of hyperspectral and LiDAR data accurately in a very acceptable CPU processing time. It should be noted that the proposed method is fully automatic and there is no need to set any parameters to increase the favourability of the proposed method.