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Proceedings Paper

Characteristics of full-waveform lidar data from typical objects and its potential in point cloud classification
Author(s): Guangcai Xu; Yong Pang; Lingling Yuan; Mingyang Li; Tian Fu
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Paper Abstract

Full-Waveform Lidar systems have already been proved to have large potentialities in forest related applications. Entire backscatter signals of each emitted pulse, which give end users more controls in raw data management and interpretation process compared to traditional discrete return lidar data, are digitized and recorded by the system. Especially in the forest area, more detail information is provided using waveform data and new opportunities are inspired for point cloud classification from waveform characteristics. In this study, full-waveform data were collected by Riegl LMS Q560 system with a point density of 1.4 points/m2 in Dayekou Watershed (DYK), Gansu province, China. These small footprint airborne full-waveform lidar data were used to extract statistic information (i.e. echo half-width, amplitude and intensity) of different targets such as grass, shrub, forest and bared area, and try to classify the typical targets in test field. Non-linear least square method was adopted to fit a series Gaussian pulse to decompose the raw waveform data. Then the attributes including peak location, half-width, amplitude, intensity of each pulse were calculated. Generally, different objects response to the emitted pulse diversely, which is incarnated in the three attributes described above. The decomposed waveform data were transformed to 3D points with several related attributes. And the field survey information and the same period of the high-resolution multispectral images were used to determine the specific location and extent of different features areas (forest, bare land, grassland, construction), then get the statistic value of three attributes for the corresponding regions in the decomposed waveform data. The results showed that three statistical characteristics of different targets are different in some extent, which demonstrated their potential in point cloud classification.

Paper Details

Date Published: 4 November 2010
PDF: 8 pages
Proc. SPIE 7840, Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality, 78401P (4 November 2010); doi: 10.1117/12.872954
Show Author Affiliations
Guangcai Xu, Institute of Forest Resource and Information Technology (China)
Nanjing Forestry Univ. (China)
Yong Pang, Institute of Forest Resource and Information Technology (China)
Lingling Yuan, Jiangsu Province Jinwei Remote Sensing Data Engineering Co.,Ltd. (China)
Mingyang Li, Nanjing Forestry Univ. (China)
Tian Fu, Institute of Forest Resource and Information Technology (China)


Published in SPIE Proceedings Vol. 7840:
Sixth International Symposium on Digital Earth: Models, Algorithms, and Virtual Reality
Huadong Guo; Changlin Wang, Editor(s)

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