Detecting linear features by spatial point processes

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dc.identifier.uri http://dx.doi.org/10.15488/700
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/724
dc.contributor.author Chai, Dengfeng
dc.contributor.author Schmidt, Alena
dc.contributor.author Heipke, Christian
dc.date.accessioned 2016-11-21T08:37:57Z
dc.date.available 2016-11-21T08:37:57Z
dc.date.issued 2016
dc.identifier.citation Chai, D.; Schmidt, A.; Heipke, C.: Detecting linear features by spatial point processes. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 841-848. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B3-841-2016
dc.description.abstract This paper proposes a novel approach for linear feature detection. The contribution is twofold: a novel model for spatial point processes and a new method for linear feature detection. It describes a linear feature as a string of points, represents all features in an image as a configuration of a spatial point process, and formulates feature detection as finding the optimal configuration of a spatial point process. Further, a prior term is proposed to favor straight linear configurations, and a data term is constructed to superpose the points on linear features. The proposed approach extracts straight linear features in a global framework. The paper reports ongoing work. As demonstrated in preliminary experiments, globally optimal linear features can be detected. eng
dc.description.sponsorship National Natural Science Foundation of China/41071263
dc.description.sponsorship National Natural Science Foundation of China/41571335
dc.description.sponsorship Zhejiang Provincial Natural Science Foundation of China/LY13D010003
dc.description.sponsorship Key Laboratory for National Geographic Census and Monitoring National Administration of Surveying, Mapping and Geoinformation/2014NGCM
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016, 12–19 July 2016, Prague, Czech Republic
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016)
dc.rights CC BY 3.0
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject Feature Detection eng
dc.subject Global Optimization eng
dc.subject Linear Feature eng
dc.subject Markov Chain Monte Carlo eng
dc.subject Simulated Annealing eng
dc.subject Spatial Point Processes eng
dc.subject Global optimization eng
dc.subject Markov processes eng
dc.subject Remote sensing eng
dc.subject Simulated annealing eng
dc.subject Data terms eng
dc.subject Feature detection eng
dc.subject Linear configuration eng
dc.subject Linear feature eng
dc.subject Markov Chain Monte-Carlo eng
dc.subject Spatial point process eng
dc.subject Feature extraction eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.subject.ddc 520 | Astronomie, Kartographie ger
dc.title Detecting linear features by spatial point processes
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 1682-1750
dc.relation.doi http://dx.doi.org/10.5194/isprsarchives-XLI-B3-841-2016
dc.bibliographicCitation.volume 41
dc.bibliographicCitation.firstPage 841
dc.bibliographicCitation.lastPage 848
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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