Airborne lidar systems have become an alternative source for the acquisition of altimeter data. In addition to multi-echo laser scanner systems, full-waveform systems are able to record the whole backscattered signal for each emitted laser pulse. These data provide more information about the structure and the physical properties of the surface. This paper is focused on the classification of full-waveform lidar and airborne image data on urban scenes. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they provide measures of variable importance for each class. This is crucial to analyze the relevance of each feature for the classification of urban scenes. Random Forests provide more accurate results than Support Vector Machines with an overall accuracy of 95.75%. The most relevant features show the contribution of lidar waveforms for classifying dense urban scenes and improve the classification accuracy for all classes.
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