dc.identifier.uri |
http://dx.doi.org/10.15488/890 |
|
dc.identifier.uri |
http://www.repo.uni-hannover.de/handle/123456789/914 |
|
dc.contributor.author |
Schmidt, Alena
|
|
dc.contributor.author |
Rottensteiner, Franz
|
|
dc.contributor.author |
Sörgel, Uwe
|
|
dc.contributor.author |
Heipke, Christian
|
|
dc.contributor.editor |
Paparoditis, N.
|
|
dc.contributor.editor |
Schindler, K.
|
|
dc.date.accessioned |
2016-12-21T10:56:41Z |
|
dc.date.available |
2016-12-21T10:56:41Z |
|
dc.date.issued |
2014 |
|
dc.identifier.citation |
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 |
|
dc.description.abstract |
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. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Göttingen : Copernicus GmbH |
|
dc.relation.ispartof |
ISPRS Technical Commission III Symposium : 5 – 7 September 2014, Zurich, Switzerland |
|
dc.relation.ispartofseries |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XL-3 |
|
dc.rights |
CC BY 3.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/3.0/ |
|
dc.subject |
Coast |
eng |
dc.subject |
Lidar |
eng |
dc.subject |
Marked point processes |
eng |
dc.subject |
Networks |
eng |
dc.subject |
Coastal zones |
eng |
dc.subject |
Extraction |
eng |
dc.subject |
Geometry |
eng |
dc.subject |
Markov processes |
eng |
dc.subject |
Networks (circuits) |
eng |
dc.subject |
Optimization |
eng |
dc.subject |
Simulated annealing |
eng |
dc.subject |
Automatic extraction |
eng |
dc.subject |
Marked point process |
eng |
dc.subject |
Non-overlapping areas |
eng |
dc.subject |
Reversible jump Markov chain Monte Carlo |
eng |
dc.subject |
RJMCMC |
eng |
dc.subject |
Stochastic optimizations |
eng |
dc.subject |
Straight-line segments |
eng |
dc.subject |
Tidal channel networks |
eng |
dc.subject |
Optical radar |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
550 | Geowissenschaften
|
ger |
dc.subject.ddc |
510 | Mathematik
|
ger |
dc.title |
Extraction of fluvial networks in lidar data using marked point processes |
|
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
2194-9034 |
|
dc.relation.issn |
1682-1750 |
|
dc.relation.doi |
https://doi.org/10.5194/isprsarchives-XL-3-297-2014 |
|
dc.relation.doi |
https://doi.org/10.5194/isprsarchives-xl-3-297-2014 |
|
dc.bibliographicCitation.issue |
3 |
|
dc.bibliographicCitation.volume |
XL-3 |
|
dc.bibliographicCitation.firstPage |
297 |
|
dc.bibliographicCitation.lastPage |
304 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|