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Journal of Applied Remote Sensing

Adaptive algorithm for classifying LiDAR data into water and land points by multifeature statistics
Author(s): Li-Xin Wei; Biao Yang; Jian-Ping Jiang; Guan-Zhong Cao
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Paper Abstract

Classifying light detection and ranging (LiDAR) data into water and land points is an issue for the application of low-attitude airborne LiDAR (e.g., digital terrain model generation and river shoreline extraction). To solve the problem of distinguishing the water points from the land points in complex landscapes, an adaptive classification algorithm of water LiDAR point clouds is proposed, which consists of the following steps. First, the descriptors of local terrain slope and point density are designed by analyzing the characteristics of low-altitude airborne LiDAR water point clouds. Then Bayes’ theorem is introduced to establish membership functions of the elevation, slope, and density. Next, the adaptive weights of the individual membership functions are determined according to the t-test of the independent samples of water and land points. Finally, a classification model based on multifeature statistics is obtained, and the adaptive classification threshold of the model is determined by the probability density of the training samples. Typical experiments conducted in the middle-lower Yangtze River riparian zone indicate that water classification accuracies higher than 99% are obtained by this algorithm, even in complex landscapes with mudflats and inland plains.

Paper Details

Date Published: 1 December 2016
PDF: 11 pages
J. Appl. Rem. Sens. 10(4) 045020 doi: 10.1117/1.JRS.10.045020
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Li-Xin Wei, Hohai Univ. (China)
Changjiang Water Resources Commission (China)
Biao Yang, Hohai Univ. (China)
Jian-Ping Jiang, Changjiang Water Resources Commission (China)
Guan-Zhong Cao, Changjiang Water Resources Commission (China)

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