Unsupervised Classification of Raw Full-Waveform Airborne Lidar Data by Self Organizing Maps

Abstract

The paper proposes a procedure based on Kohonen’s Self Organizing Maps (SOMs) to perform the unsupervised classification of raw full-waveform airborne LIDAR (Light Detection and Ranging) data, without the need of extracting features from them, that is without any preprocessing. The proposed algorithm allows the classification of points into three classes (“grass”, “trees” and “road”) in two subsequent stages. During the first one, all the raw data are given as input to a SOM and points belonging to the category “trees” are extracted on the basis of the number of peaks that characterize the waveforms. In the second stage, data not previously classified as “trees” are used to create a new SOM that, together with a hierarchical clustering algorithm, allows to distinguish between the classes “road” and “grass”. Experiments carried out show that raw full-waveform LIDAR data were classified with an overall accuracy of 93.9%, 92.5% and 92.9%, respectively.