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《Yangtze River》 2018-18
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Research on self-adaptive classification algorithm of water point clouds by low-altitude airborne LiDAR

ZHOU Jianhong;FENG Chuanyong;YANG Biao;Bureau of Hydrology,Changjiang Water Resources Commission;School of Earth Sciences and Engineering,Hohai University;  
Water and land point clouds classification is a new issue for the application of low-attitude airborne Li DAR,such as DTM generation and river shoreline extraction. However,for some complex landscapes,distinguishing the water points from the land points is difficult. An self-adaptive classification algorithm of water Li DAR point clouds by multivariate feature statistics is proposed in this paper. Firstly,the operators of local terrain slope and points density are adopted according to the characteristics of low-altitude airborne Li DAR water point clouds. Then,Bayes' theorem is used to construct membership functions of the elevation,slope and density. Then,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 multivariate feature statistics is obtained,and the adaptive classification threshold of the model is determined by the probability density of the training samples. Typical experiments indicate that water classication accuracies higher than 99% can be obtained by this algorithm,even in complex landscapes with mudflat and inland plain.
【Fund】: 国家自然科学基金项目(51420125014)
【CateGory Index】: P23
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