Classification of settlement areas in remote sensing imagery using conditional random fields

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dc.identifier.uri http://dx.doi.org/10.15488/1114
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1138
dc.contributor.author Hoberg, Thorsten
dc.contributor.author Rottensteiner, Franz
dc.date.accessioned 2017-02-03T11:49:03Z
dc.date.available 2017-02-03T11:49:03Z
dc.date.issued 2010
dc.identifier.citation Hoberg, T.; Rottensteiner, F.: Classification of settlement areas in remote sensing imagery using conditional random fields. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [100 Years ISPRS Advancing Remote Sensing Science, Pt 1] 38 (2010), Nr. 7A, S. 53-58
dc.description.abstract Land cover classification plays a key role for various geo-based applications. Numerous approaches for the classification of settlements in remote sensing imagery have been developed. Most of them assume the features of neighbouring image sites to be conditionally independent. Using spatial context information may enhance classification accuracy, because dependencies of neighbouring areas are taken into account. Conditional Random Fields (CRF) have become popular in the field of pattern recognition for incorporating contextual information because of their ability to model dependencies not only between the class labels of neighbouring image sites, but also between the labels and the image features. In this work we investigate the potential of CRF for the classification of settlements in high resolution satellite imagery. To highlight the power of CRF, tests were carried out using only a minimum set of features and a simple model of context. Experiments were performed on an Ikonos scene of a rural area in Germany. In our experiments, completeness and correctness values of 90% and better could be achieved, the CRF approach was clearly outperforming a standard Maximum-Likelihood-classification based on the same set of features. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof ISPRS Technical Commission VII Symposium - 100 Years ISPRS - Advancing Remote Sensing Science, July 05-07, 2010, Vienna, Austria
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [100 Years ISPRS Advancing Remote Sensing Science, Pt 1] 38 (2010), Nr. 7A
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Conditional Random Fields eng
dc.subject contextual information eng
dc.subject classification eng
dc.subject satellite imagery eng
dc.subject urban area eng
dc.subject markov random-field eng
dc.subject urban areas eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Classification of settlement areas in remote sensing imagery using conditional random fields
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 2194-9034
dc.bibliographicCitation.issue 7A
dc.bibliographicCitation.volume 38
dc.bibliographicCitation.firstPage 53
dc.bibliographicCitation.lastPage 58
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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