Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data

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dc.identifier.uri http://dx.doi.org/10.15488/983
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1007
dc.contributor.author Zhang, Z.
dc.contributor.author Yang, M.Y.
dc.contributor.author Zhoua, M.
dc.date.accessioned 2016-12-22T10:06:50Z
dc.date.available 2016-12-22T10:06:50Z
dc.date.issued 2013
dc.identifier.citation Zhang, Z.; Yang, M.Y.; Zhoua, M.: Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2013), Nr. 1W1, S. 389-392. DOI: https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013
dc.description.abstract Feature fusion of remote sensing images and LiDAR points cloud data, which have strong complementarity, can effectively play the advantages of multi-class features to provide more reliable information support for the remote sensing applications, such as object classification and recognition. In this paper, we introduce a novel multi-source hierarchical conditional random field (MSHCRF) model to fuse features extracted from remote sensing images and LiDAR data for image classification. Firstly, typical features are selected to obtain the interest regions from multi-source data, then MSHCRF model is constructed to exploit up the features, category compatibility of images and the category consistency of multi-source data based on the regions, and the outputs of the model represents the optimal results of the image classification. Competitive results demonstrate the precision and robustness of the proposed method. eng
dc.language.iso eng
dc.publisher Hannover : International Society for Photogrammetry and Remote Sensing
dc.relation.ispartof ISPRS Hannover Workshop 2013, 21-24 May 2013
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2013)
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Feature fusion eng
dc.subject Hierarchical model eng
dc.subject Multi-source data eng
dc.subject Classification (of information) eng
dc.subject Hierarchical systems eng
dc.subject Image fusion eng
dc.subject Image reconstruction eng
dc.subject Optical radar eng
dc.subject Random processes eng
dc.subject Remote sensing eng
dc.subject Conditional random field eng
dc.subject Information support eng
dc.subject Multisource data eng
dc.subject Object classification eng
dc.subject Remote sensing applications eng
dc.subject Remote sensing images eng
dc.subject Image classification eng
dc.subject.ddc 000 | Informatik, Informationswissenschaft, allgemeine Werke ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Multi-source hierarchical conditional random field model for feature fusion of remote sensing images and LiDAR data
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 16821750
dc.relation.doi https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013
dc.bibliographicCitation.issue 1W1
dc.bibliographicCitation.volume 40
dc.bibliographicCitation.firstPage 389
dc.bibliographicCitation.lastPage 392
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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