Land Use Classification Using Conditional Random Fields for the Verification of Geospatial Databases

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dc.identifier.uri http://dx.doi.org/10.15488/5013
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/5057
dc.contributor.author Albert, Lena
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Heipke, Christian
dc.date.accessioned 2019-06-26T12:21:09Z
dc.date.available 2019-06-26T12:21:09Z
dc.date.issued 2014
dc.identifier.citation Albert, Lena; Rottensteiner, Franz; Heipke, Christian: Land Use Classification Using Conditional Random Fields for the Verification of Geospatial Databases. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (2014), S. 1-7. DOI: https://doi.org/10.5194/isprsannals-ii-4-1-2014
dc.description.abstract Geospatial land use databases contain important information with high benefit for several users, especially when they provide a detailed description on parcel level. Due to many changes connected with a high effort of the update process, these large-scale land use maps become outdated quickly. This paper presents a two-step approach for the automatic verification of land use objects of a geospatial database using high-resolution aerial images. In the first step, a precise pixel-based land cover classification using spectral, textural and three-dimensional features is applied. In the second step, an object-based land use classification follows, which is based on features derived from the pixel-based land cover classification as well as geometrical, spectral and textural features. For both steps, the potential of the incorporation of contextual knowledge in the classification process is explored. For this purpose, we use Conditional Random Fields (CRF), which have proven to be a flexible, powerful framework for contextual classification in various applications in remote sensing. The results of the approach are evaluated on an urban test site and the influence of different features and models on the classification accuracy is analysed. It is shown that the use of CRF for the land cover classification yields an improved accuracy and smoother results compared to independent pixel-based approaches. The integration of contextual knowledge also has a remarkable positive effect on the results of the land use classification. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof ISPRS Technical Commission IV Symposium, 14 – 16 May 2014, Suzhou, China
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (2014)
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Data mining eng
dc.subject Conditional random field eng
dc.subject Artificial intelligence eng
dc.subject Pixel eng
dc.subject Spatial database eng
dc.subject Land cover eng
dc.subject Computer vision eng
dc.subject Computer science eng
dc.subject Land use eng
dc.subject Remote sensing eng
dc.subject Database eng
dc.subject Geospatial analysis eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Land Use Classification Using Conditional Random Fields for the Verification of Geospatial Databases
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 2194-9050
dc.relation.doi https://doi.org/10.5194/isprsannals-ii-4-1-2014
dc.bibliographicCitation.volume II-4
dc.bibliographicCitation.firstPage 1
dc.bibliographicCitation.lastPage 7
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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