Classifying airborne bathymetry data using the Random Forest algorithm

The information on the topography of the seabed included in data from airborne lidar bathymetry can be used for the detection of changes occurring at the bottom of the basin and for the detection of objects deposited on it. Processing of full waveform data enables obtaining data on the water surface and the identification of the underwater situation. The classification process based on the Random Forest (RF) algorithm is presented using data from lidar bathymetry. The classification was performed in two independent approaches using input vector consisting of 16 features. In the first approach, the entire point cloud was classified, in the second the point cloud did not contain points of the water surface. In the classification, traits based on the full waveform and resulting from point cloud geometry were used. The quantitative efficiency of classification was verified through error matrices. The obtained efficiency (100% water surface, 99.9% seabed and 60% objects) of the point classification of the objects enables the possibility for using the RF algorithm for detection of objects on the seabed. In comparison to Support Vector Machines, the RF algorithm has better results in the detection of points on the objects in point cloud with water surface.