Schmidt, A.; Rottensteiner, F.; Soergel, U.; Heipke, C.: Extraction of fluvial networks in lidar data using marked point processes. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2014), Nr. 3, S. 297-304. DOI:
https://doi.org/10.5194/isprsarchives-XL-3-297-2014
Zusammenfassung: |
We propose a method for the automatic extraction of fluvial networks in lidar data with the aim to obtain a connected network represented by the fluvial channels' skeleton. For that purpose we develop a two-step approach. First, we fit rectangles to the data using a stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. High gradients on the rectangles' border and non-overlapping areas of the objects are introduced as model in the optimization process. In a second step, we determine the principal axes of the rectangles and their intersection points. Based on this a network graph is constructed in which nodes represent junction points or end points, respectively, and edges in-between straight line segments. We evaluate our method on lidar data with a tidal channel network and show some preliminary results.
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Lizenzbestimmungen: |
CC BY 3.0 Unported - https://creativecommons.org/licenses/by/3.0/
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Publikationstyp: |
Article |
Publikationsstatus: |
publishedVersion |
Erstveröffentlichung: |
2014 |
Schlagwörter (englisch): |
Coast, Lidar, Marked point processes, Networks, Coastal zones, Extraction, Geometry, Markov processes, Networks (circuits), Optimization, Simulated annealing, Automatic extraction, Marked point process, Non-overlapping areas, Reversible jump Markov chain Monte Carlo, RJMCMC, Stochastic optimizations, Straight-line segments, Tidal channel networks, Optical radar
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Fachliche Zuordnung (DDC): |
550 | Geowissenschaften, 510 | Mathematik
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Kontrollierte Schlagwörter: |
Konferenzschrift
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