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: Heipke, C.; Jacobsen, K.; Rottensteiner, F.; Sörgel, U. (Eds.): ISPRS Hannover Workshop 2013. Katlenburg-Lindau : Copernicus Publications, 2013 (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 40-1/W1), 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: 144




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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 Unported
Document Type: bookPart
Publishing status: publishedVersion
Issue Date: 2013
Appears in Collections:Fakultät für Elektrotechnik und Informatik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 115 79.86%
2 image of flag of China China 7 4.86%
3 image of flag of No geo information available No geo information available 5 3.47%
4 image of flag of Poland Poland 3 2.08%
5 image of flag of United States United States 2 1.39%
6 image of flag of Korea, Republic of Korea, Republic of 2 1.39%
7 image of flag of Austria Austria 2 1.39%
8 image of flag of Vietnam Vietnam 1 0.69%
9 image of flag of Peru Peru 1 0.69%
10 image of flag of Mexico Mexico 1 0.69%
    other countries 5 3.47%

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