@inproceedings{a90d950e08304386a5bf79fea3b6cf2f,
title = "A kernel-density-estimation-based outlier detection for airborne LiDAR point clouds",
abstract = "An outlier detection method is proposed based on the kernel density estimation for removing the outliers in airborne LiDAR point clouds. The point cloud is divided into many blocks. Then, in each block, the kernel probability density of the height values of all points is estimated. Two elevation thresholds, one for low outliers and one for high outliers, are selected based on the values of the probability density and the values of elevation. The computation is simplified in complexity for the method doses not focus on the calculation of individual points. Two datasets were utilized to test our method. This method combines distance-based method with density-based method. Experiments showed that our proposed method had good performance.",
keywords = "airborne LiDAR, kernel density estimation, outlier, point cloud",
author = "Xiangrui Tian and Lijun Xu and Xiaolu Li and Lili Jing and Yan Zhao",
year = "2012",
doi = "10.1109/IST.2012.6295546",
language = "英语",
isbn = "9781457717741",
series = "IST 2012 - 2012 IEEE International Conference on Imaging Systems and Techniques, Proceedings",
pages = "263--266",
booktitle = "IST 2012 - 2012 IEEE International Conference on Imaging Systems and Techniques, Proceedings",
note = "2012 IEEE International Conference on Imaging Systems and Techniques, IST 2012 ; Conference date: 16-07-2012 Through 17-07-2012",
}