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

Abstract

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

Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

“…One method using Bayesian Networks shows how digital terrain models can be derived automatically from HS and LiDAR data. 11 All supervised classification tasks rely on ground truth data, which can be difficult or costly to gather. Especially if human analysts are involved, active learning can be used to alleviate some of the need for more ground truth data and help reduce the training costs.…”

Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment

“…Remote sensing technologies are already widely used in military context [2,8,18]. Flying platforms and their corresponding sensor-(systems) experienced a fast development during the last decade [10,13,16], mainly driven by the establishment of drone technology, active 3D point measurements and the increasing number of operating sensor channels. Passive sensors with the highest number of imaging bands are the so-called hyperspectral (HS) sensors or spectrometers.…”