ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 293-300, 2015
https://doi.org/10.5194/isprsannals-II-3-W4-293-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
11 Mar 2015
MULTI-SOURCE MULTI-SCALE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR REMOTE SENSING IMAGE CLASSIFICATION
Z. Zhang1, M. Y. Yang2, and M. Zhou1 1Academy of OptoElectronics, Chinese Academy of Sciences, Beijing, China
2Institute for Information Processing (TNT), Leibniz University Hannover, Germany
Keywords: Classification, Fusion, Multisensor, LIDAR, Hierarchical, Vision, Performance 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.
Conference paper (PDF, 2124 KB)


Citation: Zhang, Z., Yang, M. Y., and Zhou, M.: MULTI-SOURCE MULTI-SCALE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR REMOTE SENSING IMAGE CLASSIFICATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 293-300, https://doi.org/10.5194/isprsannals-II-3-W4-293-2015, 2015.

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