Image and Signal Processing Methods

Adaptive algorithm for classifying LiDAR data into water and land points by multifeature statistics

[+] Author Affiliations
Li-Xin Wei

Hohai University, Institute of Remote Sensing and Spatial Information Engineering, School of Earth Sciences and Engineering, No. 8 Focheng West Road, Nanjing 211100, China

Changjiang Water Resources Commission, Lower Changjiang River Bureau of Hydrological and Water Resources Survey, No. 66 Dama Road, Nanjing 210011, China

Biao Yang

Hohai University, Institute of Remote Sensing and Spatial Information Engineering, School of Earth Sciences and Engineering, No. 8 Focheng West Road, Nanjing 211100, China

Jian-Ping Jiang, Guan-Zhong Cao

Changjiang Water Resources Commission, Lower Changjiang River Bureau of Hydrological and Water Resources Survey, No. 66 Dama Road, Nanjing 210011, China

J. Appl. Remote Sens. 10(4), 045020 (Dec 01, 2016). doi:10.1117/1.JRS.10.045020
History: Received October 7, 2016; Accepted November 14, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Topics

LIDAR ; Water

Citation

Li-Xin Wei ; Biao Yang ; Jian-Ping Jiang and Guan-Zhong Cao
"Adaptive algorithm for classifying LiDAR data into water and land points by multifeature statistics", J. Appl. Remote Sens. 10(4), 045020 (Dec 01, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.045020


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