期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 54, 期 12, 页码 7309-7322出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2016.2599163
关键词
Airborne laser scanning (ALS) point clouds; classification; discriminative dictionary learning; point clusters; sparse coding
类别
资金
- National Natural Science Foundation of China [41371324, 41325005, 41501499]
- Shenzhen Scientific Research and Development Funding Program [JCYJ20150625102531697]
Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification.
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