Ground target extraction using airborne streak tube imaging LiDAR

被引:4
作者
Dong, Zhiwei [1 ]
Yan, Yongji [1 ]
Jiang, Yugang [2 ]
Fan, Rongwei [1 ]
Chen, Deying [1 ]
机构
[1] Harbin Inst Technol, Natl Key Lab Sci & Technol Tunable Laser, Harbin, Peoples R China
[2] Tianjin Jinhang Tech Phys Inst, Tianjin Key Lab Opt Thin Film, Tianjin, Peoples R China
关键词
airborne LiDAR; streak tube; ground target extraction; STIL; RECOGNITION;
D O I
10.1117/1.JRS.15.016509
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Airborne LiDAR has become a kind of indispensable measurement device in the current field of remote sensing. However, target extraction using traditional airborne LiDAR based on single-point scanning requires filtering and point cloud segmentation operations, which are complicated and time consuming. Although some researchers have studied streak tube imaging LiDAR (STIL) before, there are few reports in which it is used as an airborne LiDAR for ground measurement in large-scale field. We propose a method of ground target extraction using STIL. Taking advantage of the structural properties of the STIL, complex filtering and point cloud segmentation algorithms are avoided in the target extraction method. The purpose of this article is to verify the feasibility of airborne STIL in ground target extraction. We analyzed the raw streak signal image collected by field experiment and used morphology and intensity information to extract features. After that, we employed the decision tree classifier to classify the four kinds of targets and evaluated the extraction results. The results show that the target extraction achieved satisfactory consequences under an acceptable level. That demonstrates that ground target extraction using STIL is feasible in the field of large-scale remote sensing. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:14
相关论文
共 27 条
[1]   Automatic Object Extraction from Electrical Substation Point Clouds [J].
Arastounia, Mostafa ;
Lichti, Derek D. .
REMOTE SENSING, 2015, 7 (11) :15605-15629
[2]   Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data [J].
Arastounia, Mostafa .
REMOTE SENSING, 2015, 7 (11) :14916-14938
[3]   The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory [J].
Ayrey, Elias ;
Hayes, Daniel J. .
REMOTE SENSING, 2018, 10 (04)
[4]  
Brock JC, 2001, PHOTOGRAMM ENG REM S, V67, P1245
[5]   Research on the streak tube three-dimensional imaging method based on compressive sensing [J].
Cao, Jingya ;
Han, Shaokun ;
Liu, Fei ;
Zhai, Yu ;
Xia, Wenze .
OPTICAL ENGINEERING, 2018, 57 (08)
[6]   Roof plane extraction from airborne lidar point clouds [J].
Cao, Rujun ;
Zhang, Yongjun ;
Liu, Xinyi ;
Zhao, Zongze .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (12) :3684-3703
[7]   The effectiveness of airborne LiDAR data in the recognition of channel-bed morphology [J].
Cavalli, Marco ;
Tarolli, Paolo ;
Marchi, Lorenzo ;
Fontana, Giancarlo Dalla .
CATENA, 2008, 73 (03) :249-260
[8]   A three-step technique of robust line detection with modified Hough Transform [J].
Di, HJ ;
Wang, L ;
Xu, GY .
THIRD INTERNATIONAL SYMPOSIUM ON MULTISPECTRAL IMAGE PROCESSING AND PATTERN RECOGNITION, PTS 1 AND 2, 2003, 5286 :835-838
[9]   Research of elevation error based on points cloud data of stripe principle LiDAR [J].
Dong Zhi-wei ;
Wang Zheng-guo ;
Chen Mo-ran ;
Fan Rong-wei ;
Li Xu-dong ;
Chen De-ying ;
Yu Xin ;
Zhang Rui-huan ;
Ma Yu-fei .
INTERNATIONAL CONFERENCE ON OPTOELECTRONICS AND MICROELECTRONICS TECHNOLOGY AND APPLICATION, 2017, 10244
[10]   Review of studies on tree species classification from remotely sensed data [J].
Fassnacht, Fabian Ewald ;
Latifi, Hooman ;
Sterenczak, Krzysztof ;
Modzelewska, Aneta ;
Lefsky, Michael ;
Waser, Lars T. ;
Straub, Christoph ;
Ghosh, Aniruddha .
REMOTE SENSING OF ENVIRONMENT, 2016, 186 :64-87