Share Email Print
cover

Proceedings Paper

Detection of fault structures with airborne LiDAR point-cloud data
Author(s): Jie Chen; Lei Du
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The airborne LiDAR (Light Detection And Ranging) technology is a new type of aerial earth observation method which can be used to produce high-precision DEM (Digital Elevation Model) quickly and reflect ground surface information directly. Fault structure is one of the key forms of crustal movement, and its quantitative description is the key to the research of crustal movement. The airborne LiDAR point-cloud data is used to detect and extract fault structures automatically based on linear extension, elevation mutation and slope abnormal characteristics. Firstly, the LiDAR point-cloud data is processed to filter out buildings, vegetation and other non-surface information with the TIN (Triangulated Irregular Network) filtering method and Burman model calibration method. TIN and DEM are made from the processed data sequentially. Secondly, linear fault structures are extracted based on dual-threshold method. Finally, high-precision DOM (Digital Orthophoto Map) and other geological knowledge are used to check the accuracy of fault structure extraction. An experiment is carried out in Beiya Village of Yunnan Province, China. With LiDAR technology, results reveal that: the airborne LiDAR point-cloud data can be utilized to extract linear fault structures accurately and automatically, measure information such as height, width and slope of fault structures with high precision, and detect faults in areas with vegetation coverage effectively.

Paper Details

Date Published: 6 August 2015
PDF: 8 pages
Proc. SPIE 9669, Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China, 96690G (6 August 2015); doi: 10.1117/12.2204924
Show Author Affiliations
Jie Chen, China Aero Geophysical Survey and Remote Sensing Ctr. for Land and Resources (China)
Lei Du, China Aero Geophysical Survey and Remote Sensing Ctr. for Land and Resources (China)


Published in SPIE Proceedings Vol. 9669:
Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China
Qingxi Tong; Boqin Zhu, Editor(s)

© SPIE. Terms of Use
Back to Top