One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm
AbstractAutomatic classification of light detection and ranging (LiDAR) data in urban areas is of great importance for many applications such as generating three-dimensional (3D) building models and monitoring power lines. Traditional supervised classification methods require training samples of all classes to construct a reliable classifier. However, complete training samples are normally hard and costly to collect, and a common circumstance is that only training samples for a class of interest are available, in which traditional supervised classification methods may be inappropriate. In this study, we investigated the possibility of using a novel one-class classification algorithm, i.e., the presence and background learning (PBL) algorithm, to classify LiDAR data in an urban scenario. The results demonstrated that the PBL algorithm implemented by back propagation (BP) neural network (PBL-BP) could effectively classify a single class (e.g., building, tree, terrain, power line, and others) from airborne LiDAR point cloud with very high accuracy. The mean F-score for all of the classes from the PBL-BP classification results was 0.94, which was higher than those from one-class support vector machine (SVM), biased SVM, and maximum entropy methods (0.68, 0.82 and 0.93, respectively). Moreover, the PBL-BP algorithm yielded a comparable overall accuracy to the multi-class SVM method. Therefore, this method is very promising in the classification of the LiDAR point cloud. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Ao, Z.; Su, Y.; Li, W.; Guo, Q.; Zhang, J. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm. Remote Sens. 2017, 9, 1001.
Ao Z, Su Y, Li W, Guo Q, Zhang J. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm. Remote Sensing. 2017; 9(10):1001.Chicago/Turabian Style
Ao, Zurui; Su, Yanjun; Li, Wenkai; Guo, Qinghua; Zhang, Jing. 2017. "One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm." Remote Sens. 9, no. 10: 1001.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.