Volume XL-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 389-392, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W1, 389-392, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W1-389-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 May 2013

02 May 2013

MULTI-SOURCE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR FEATURE FUSION OF REMOTE SENSING IMAGES AND LIDAR DATA

Z. Zhang, M. Y. Yang, and M. Zhou Z. Zhang et al.
  • Key Laboratory of Quantitative Remote Sensing Information Technology, Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing, China
  • Institute for Information Processing (TNT), Leibniz University Hannover, Germany

Keywords: Feature fusion, Conditional Random Field, Image Classification, Multi-source Data, Hierarchical model

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.