One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm
文献类型：期刊论文
| 作者 | Ao, Zurui; Su, Yanjun3,4; Li, Wenkai1; Guo, Qinghua3 ; Zhang, Jing
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2017 |
| 卷号 | 9期号:10 |
| 关键词 | LiDAR one-class classification presence and background learning algorithm remote sensing |
| DOI | 10.1104/pp.16.01305 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| 学科主题 | Plant Sciences |
| 出版地 | BASEL |
| 电子版国际标准刊号 | 2072-4292 |
| WOS关键词 | LAND-COVER CLASSIFICATION ; SAMPLE SELECTION ; SVM ; ENSEMBLE ; SUPPORT ; INFORMATION ; RESOLUTION ; MODELS ; MAXENT |
| 语种 | 英语 |
| WOS记录号 | WOS:000394140800043 |
| 出版者 | MDPI |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/22311]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Univ Calif Merced, Sierra Nevada Res Inst, Sch Engn, Merced, CA 95343 USA 2.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China 3.Capital Normal Univ, Sch Resources, Key Lab Informat Acquisit D, Educ Minist,Sch Resources Environm & Tourism, Beijing 100048, Peoples R China 4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ao, Zurui,Su, Yanjun,Li, Wenkai,et al. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm[J]. REMOTE SENSING,2017,9(10). |
| APA | 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 SENSING,9(10). |
| MLA | Ao, Zurui,et al."One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm".REMOTE SENSING 9.10(2017). |
入库方式： OAI收割
来源：植物研究所
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