Volume II-3/W4
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.
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

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 Z. Zhang et al.
  • 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.