张振新等:Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification
被阅读 680 次
2016-11-21
Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification
作者:Zhang, ZX (Zhang, Zhenxin)[ 1 ] ; Zhang, LQ (Zhang, Liqiang)[ 1 ] ; Tong, XH (Tong, Xiaohua)[ 2 ] ; Guo, B (Guo, Bo)[ 3,4 ] ; Zhang, L (Zhang, Liang)[ 1 ] ; Xing, XY (Xing, Xiaoyue)[ 1 ]
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷: 54  期: 12  页: 7309-7322
DOI: 10.1109/TGRS.2016.2599163
出版年: DEC 2016
 
摘要
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.
 
通讯作者地址: Zhang, LQ (通讯作者)
Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Tongji Univ, Sch Surveying & Geoinformat, Shanghai 200092, Peoples R China
[ 3 ] Shenzhen Univ, Natl Adm Surveying Mapping & GeoInformat, Key Lab Geoenvironm Monitoring Coastal Zone, Shenzhen 518060, Peoples R China
[ 4 ] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing, Shenzhen 518060, Peoples R China