MULTI-SOURCE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR FEATURE FUSION OF REMOTE SENSING IMAGES AND LIDAR DATA
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 th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2013-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W1/389/2013/isprsarchives-XL-1-W1-389-2013.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |