Abstract
Automatic 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.
Highlights
Airborne light detection and ranging (LiDAR) is becoming a important technology for the acquisition of accurate three-dimensional (3D) spatial data [1], with applications including land cover surveys [2], forestry parameter estimation [3], and 3D city modeling [4]
Our experiment results indicated that maximum entropy (MaxEnt) outperformed biased SVM (BSVM), which is different from the findings reported by Mack and Waske (2017) [57] and Stenzel et al (2017) [58]
This study investigated the classification of LiDAR data using a novel one-class classification method, i.e., presence and background learning (PBL) algorithm
Summary
Airborne light detection and ranging (LiDAR) is becoming a important technology for the acquisition of accurate three-dimensional (3D) spatial data [1], with applications including land cover surveys [2], forestry parameter estimation [3], and 3D city modeling [4]. Since LiDAR data are discrete unstructured points, classification of LiDAR point cloud is an essential procedure before further data processing and model construction. Point-based supervised classification methods based on Random Forests [6], decision trees [7], AdaBoost [8], and JointBoost [9] have yielded satisfactory results in LiDAR point cloud classification. In traditional supervised classification methods, the completeness and representativeness of training sets are of vital importance to the classification accuracy. There are circumstances that we only concern about one specific class, regardless of other classes In such cases, it is hard or not necessary to collect training sets for classes other than the target class, and traditional supervised classification methods might be inaccurate due to incomplete training sets
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