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

: Perpeet, Dominik; Groß, Wolfgang; Middelmann, Wolfgang

Postprint urn:nbn:de:0011-n-2187820 (1.2 MByte PDF)
MD5 Fingerprint: c44b4577aacde384a3487131eee6629f
Copyright 2012 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Created on: 13.11.2012

Bruzzone, L. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Image and Signal Processing for Remote Sensing XVIII : Edinburgh 2012, 24.09.2012, Edinburgh, United Kingdom
Bellingham, WA: SPIE, 2012 (Proceedings of SPIE 8537)
ISBN: 978-0-8194-9277-7
Paper 85370W
Conference "Image and Signal Processing for Remote Sensing" <18, 2012, Edinburgh>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
hyperspectral; DTM; Bayesian network; automatic; LiDAR; data fusion

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.