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

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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

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/983

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Sum total of downloads: 70




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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.
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2013
Appears in Collections:Fakultät für Elektrotechnik und Informatik

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pos. country downloads
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1 image of flag of Germany Germany 60 85.71%
2 image of flag of China China 6 8.57%
3 image of flag of Vietnam Vietnam 1 1.43%
4 image of flag of Turkey Turkey 1 1.43%
5 image of flag of Mexico Mexico 1 1.43%
6 image of flag of Italy Italy 1 1.43%

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