Open AccessVOLUME BASED DTM GENERATION FROM VERY HIGH RESOLUTION PHOTOGRAMMETRIC DSMS
Author(s) -
B. Piltz,
S. Bayer,
A. M. Poznanska
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-83-2016
Subject(s) - terrain , metric (unit) , digital elevation model , volume (thermodynamics) , photogrammetry , algorithm , mathematics , computer science , remote sensing , geology , computer vision , physics , geography , engineering , operations management , cartography , quantum mechanics
In this paper we propose a new algorithm for digital terrain (DTM) model reconstruction from very high spatial resolution digital surface models (DSMs). It represents a combination of multi-directional filtering with a new metric which we call <i>normalized volume above ground</i> to create an above-ground mask containing buildings and elevated vegetation. This mask can be used to interpolate a ground-only DTM. The presented algorithm works fully automatically, requiring only the processing parameters <i>minimum height</i> and <i>maximum width</i> in metric units. Since slope and breaklines are not decisive criteria, low and smooth and even very extensive flat objects are recognized and masked. The algorithm was developed with the goal to generate the normalized DSM for automatic 3D building reconstruction and works reliably also in environments with distinct hillsides or terrace-shaped terrain where conventional methods would fail. A quantitative comparison with the ISPRS data sets <i>Potsdam</i> and <i>Vaihingen</i> show that 98-99% of all building data points are identified and can be removed, while enough ground data points (~66%) are kept to be able to reconstruct the ground surface. Additionally, we discuss the concept of <i>size dependent height thresholds</i> and present an efficient scheme for pyramidal processing of data sets reducing time complexity to linear to the number of pixels, <i>O(WH)</i>.