Large-scalewater classification of coastal areas using airborne topographic lidar data

Accurate Digital Terrain Models (DTM) are inevitable inputs for mapping areas subject to natural hazards. Topographic lidar scanning has become an established technique to characterize the Earth surface: and reconstruct the topography. For flood hazard modeling in coastal areas, the key step before terrain modeling is the discrimination of land and water surfaces within the delivered point clouds. Therefore, instantaneous shoreline, river borders, inland waters can be extracted as a basis for more reliable DTM generation. This paper presents an automatic, efficient, and versatile workflow for land/water classification of airborne topographic lidar data. For that purpose, a classification framework based on Support Vector Machines (SVM) is designed. First, a set of features, based only 3D lidar point coordinates and flightline information, is defined. Then, the SVM learning step is performed on small but well-targeted areas thanks to an automatic region growing strategy. Finally, label probabilities given by the SVM are merged during a probabilistic relaxation step in order to remove pixel-wise misclassification. Results over two large areas show that survey of millions of points are labelled with high accuracy (>95%) and that small features of interest are still well classified though we work at low point densities (2-3pts/m²). Finally, our approach provides a strong basis for further discrimination of coastal land-cover classes and habitats.