Multi-source multi-scale hierarchical conditional random field model for remote sensing image classification

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dc.identifier.uri http://dx.doi.org/10.15488/1080
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1104
dc.contributor.author Zhang, Z.
dc.contributor.author Yang, M.Y.
dc.contributor.author Zhou, M.
dc.date.accessioned 2017-02-02T13:57:10Z
dc.date.available 2017-02-02T13:57:10Z
dc.date.issued 2015
dc.identifier.citation Zhang, Z.; Yang, M. Y.; Zhou, M.: Multi-source multi-scale hierarchical conditional random field model for remote sensing image classification. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (2015), Nr. W4, S. 293-300. DOI: https://doi.org/10.5194/isprsannals-II-3-W4-293-2015
dc.description.abstract Fusion of remote sensing images and LiDAR data provides complimentary information for the remote sensing applications, such as object classification and recognition. In this paper, we propose a novel multi-source multi-scale hierarchical conditional random field (MSMSH-CRF) model to integrate features extracted from remote sensing images and LiDAR point cloud data for image classification. MSMSH-CRF model is then constructed to exploit the features, category compatibility of multi-scale images and the category consistency of multi-source data based on the regions. The output of the model represents the optimal results of the image classification. We have evaluated the precision and robustness of the proposed method on airborne data, which shows that the proposed method outperforms standard CRF method. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof Joint ISPRS Conference on Photogrammetric Image Analysis (PIA) and High Resolution Earth Imaging for Geospatial Information (HRIGI), March 25-27, 2015, Munich, Germany
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (2015), Nr. W4
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Classification eng
dc.subject Fusion eng
dc.subject Multisensor eng
dc.subject LIDAR eng
dc.subject Hierarchical eng
dc.subject Vision eng
dc.subject Performance eng
dc.subject lidar data eng
dc.subject urban areas eng
dc.subject fusion eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Multi-source multi-scale hierarchical conditional random field model for remote sensing image classification
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 2194-9034
dc.relation.doi https://doi.org/10.5194/isprsannals-II-3-W4-293-2015
dc.bibliographicCitation.issue W4
dc.bibliographicCitation.volume II-3
dc.bibliographicCitation.firstPage 293
dc.bibliographicCitation.lastPage 300
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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