Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 10, Issue 4, Pages 1651-1661Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2016.2628775
Keywords
Airborne light detection and ranging (LiDAR); Bayesian network (BN); classification; optical imagery; point cloud
Categories
Funding
- Natural Science Foundation of China [41471360]
- Fundamental Research Funds for the Central Universities [2652015176]
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Point cloud classification is of great importance to applications of airborne Light Detection And Ranging (LiDAR) data. In recent years, airborne LiDAR has been integrated with various other sensors, e.g., optical imaging sensors, and thus, the fusion of multiple data types for scene classification has become a hot topic. Therefore, this paper proposes a Bayesian network (BN) model that is suitable for airborne point cloud classification fusing multiple data types. Based on an analysis of the characteristics of LiDAR point clouds and aerial images, we first extract the geometric features from the point clouds and the spectral features from the optical images. The optimal BN structure is then trained using an improved mutual-information-based K2 algorithm to obtain the optimal BN classifier for point cloud classification. Experiments demonstrate that the BN classifier can effectively distinguish four types of basic ground objects, including ground, vegetation, trees, and buildings, with a high accuracy of over 90%. Moreover, compared with other classifiers, the proposed BN classifier can achieve the highest overall accuracies, and in particular, the classifier demonstrates its advantage in the classification of ground and low vegetation points.
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