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Proceedings Paper

Feature extraction with LIDAR data and aerial images
Author(s): Jianhua Mao; Yanjing Liu; Penggen Cheng; Xianhua Li; Qihong Zeng; Jing Xia
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Paper Abstract

Raw LIDAR data is a irregular spacing 3D point cloud including reflections from bare ground, buildings, vegetation and vehicles etc., and the first task of the data analyses of point cloud is feature extraction. However, the interpretability of LIDAR point cloud is often limited due to the fact that no object information is provided, and the complex earth topography and object morphology make it impossible for a single operator to classify all the point cloud precisely 100%. In this paper, a hierarchy method for feature extraction with LIDAR data and aerial images is discussed. The aerial images provide us information of objects figuration and spatial distribution, and hierarchic classification of features makes it easy to apply automatic filters progressively. And the experiment results show that, using this method, it was possible to detect more object information and get a better result of feature extraction than using automatic filters alone.

Paper Details

Date Published: 28 October 2006
PDF: 8 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190P (28 October 2006); doi: 10.1117/12.712927
Show Author Affiliations
Jianhua Mao, Shanghai Univ. (China)
Jiangxi Normal Univ. (China)
Yanjing Liu, Guangxi G-Energy Information Engineering Co., Ltd. (China)
Penggen Cheng, Central South Univ. (China)
Xianhua Li, Shanghai Univ. (China)
Qihong Zeng, Shanghai Univ. (China)
Jing Xia, Jiangxi Normal Univ. (China)


Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

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