Context models for crf-based classification of multitemporal remote sensing data

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dc.identifier.uri http://dx.doi.org/10.15488/5171
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/5218
dc.contributor.author Hoberg, Thorsten
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Heipke, Christian
dc.date.accessioned 2019-08-15T07:18:15Z
dc.date.available 2019-08-15T07:18:15Z
dc.date.issued 2012
dc.identifier.citation Hoberg, Thorsten; Rottensteiner, Franz; Heipke, Christian: Context models for crf-based classification of multitemporal remote sensing data. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-7 (2012), Nr. 1, S. 129-134. DOI: https://doi.org/10.5194/isprsannals-i-7-129-2012
dc.description.abstract The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover changes at a high frequency. A simultaneous classification of images from different epochs that is also capable of detecting changes is achieved by a new classification technique based on Conditional Random Fields (CRF). CRF provide a probabilistic classification framework including local spatial and temporal context. Although context is known to improve image analysis results, so far only little research was carried out on how to model it. Taking into account context is the main benefit of CRF in comparison to many other classification methods. Context can be already considered by the choice of features and in the design of the interaction potentials that model the dependencies of interacting sites in the CRF. In this paper, these aspects are more thoroughly investigated. The impact of the applied features on the classification result as well as different models for the spatial interaction potentials are evaluated and compared to the purely label-based Markov Random Field model. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-7 (2012), Nr. 1
dc.rights CC BY 3.0 Unported
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 Pattern recognition eng
dc.subject Probabilistic classification eng
dc.subject Satellite eng
dc.subject Markov random field eng
dc.subject Land cover eng
dc.subject Temporal context eng
dc.subject Computer science eng
dc.subject Remote sensing eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Context models for crf-based classification of multitemporal remote sensing data eng
dc.type Article
dc.type Text
dc.relation.issn 2194-9050
dc.relation.doi https://doi.org/10.5194/isprsannals-i-7-129-2012
dc.bibliographicCitation.issue 1
dc.bibliographicCitation.volume I-7
dc.bibliographicCitation.firstPage 129
dc.bibliographicCitation.lastPage 134
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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