Using label noise robust logistic regression for automated updating of topographic geospatial databases

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dc.identifier.uri http://dx.doi.org/10.15488/1181
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1205
dc.contributor.author Maas, Alina
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Heipke, Christian
dc.date.accessioned 2017-03-02T12:48:03Z
dc.date.available 2017-03-02T12:48:03Z
dc.date.issued 2016
dc.identifier.citation Maas, A.; Rottensteiner, F.; Heipke, C.: Using label noise robust logistic regression for automated updating of topographic geospatial databases. In: XXIII ISPRS Congress, Commission VII 3 (2016), Nr. 7, S. 133-140. DOI: https://doi.org/10.5194/isprsannals-III-7-133-2016
dc.description.abstract Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof 23rd ISPRS Congress, July 12-19 2016, Prague, Czech Republic
dc.relation.ispartofseries XXIII ISPRS Congress, Commission VII 3 (2016), Nr. 7
dc.rights CC BY 3.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject change detection eng
dc.subject label noise eng
dc.subject logistic regression eng
dc.subject supervised classification eng
dc.subject random-field model eng
dc.subject classification eng
dc.subject context eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Using label noise robust logistic regression for automated updating of topographic geospatial databases eng
dc.type article
dc.type Text
dc.relation.issn 2194-9034
dc.relation.doi https://doi.org/10.5194/isprsannals-III-7-133-2016
dc.bibliographicCitation.issue 7
dc.bibliographicCitation.volume 3
dc.bibliographicCitation.firstPage 133
dc.bibliographicCitation.lastPage 140
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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