Automatic generation of digital terrain models from LiDAR and hyperspectral data using Bayesian networks

Various tasks such as urban development, terrain mapping or waterway and drainage modeling depend on digital terrain models (DTM) from large scale remote sensing data. Usually, DTM generation is a task requiring extensive manual interference. Previous attempts for automation are mostly based on determining the non-ground regions via fixed thresholds followed by smoothing operations. Thus, we propose a novel approach to automatically deduce a DTM from a digital surface model (DSM) with the aid of hyperspectral data. For this, advantages of a line scanning LiDAR system and a pushbroom hyperspectral sensor are combined to improve the result. We construct a hybrid Bayesian network (HBN), where modeled nodes can be discrete or continuous, and incorporate our already determined features. Using this network we determine probability estimates whether each point is part of terrain obstructions. While using two different sensor types supplies robust features, Bayesian networks can be automatically trained and adapted to specific scenarios such as mountainous or urban regions