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Title
Terrain extraction in built-up areas from satellite stereo-imagery-derived surface models : a stratified object-based approach
AuthorLuethje, Fritjof ; Tiede, Dirk ; Eisank, Clemens
Published in
ISPRS International Journal of Geo-Information, 2017, Vol. 6, Issue 1, page 1-13
PublishedMDPI, 2017
LanguageEnglish
Document typeJournal Article
Keywords (EN)object-based image analysis / digital surface model / digital elevation model / stereo-imagery / transformation / automation / urban / built-up
Project-/ReportnumberG-SEXTANT FP7 312912
Project-/ReportnumberFWF Doctoral College GIScience (DK W 1237-N23)
ISSN2220-9964
URNurn:nbn:at:at-ubs:3-2982 Persistent Identifier (URN)
DOI10.3390/ijgi6010009 
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 The work is publicly available
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Terrain extraction in built-up areas from satellite stereo-imagery-derived surface models [10.34 mb]
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Abstract (English)

Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observedespecially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM andin accordance with the RUIdiffers between the different land cover classes.

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CC-BY-License (4.0)Creative Commons Attribution 4.0 International License