LARGE-SCALE WATER 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 scan-ning has become an established technique to characterize the Earth surface: and reconstruct the topography. For flood hazard model-ing in coastal areas, the key step before terrain modeling is the dis-crimination of land and water surfaces within the delivered point clouds. Therefore, instantaneous shoreline, river borders, inland wa-ters 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 ar-eas 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. Re-sults over two large areas show that survey of millions of points are labelled with high accuracy (>95%) and that small features of in-terest 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