A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION
Denoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1021/2019/isprs-archives-XLII-2-W13-1021-2019.pdf |
| _version_ | 1818562199471259648 |
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| author | Z. Hui P. Cheng L. Wang Y. Xia H. Hu X. Li |
| author_facet | Z. Hui P. Cheng L. Wang Y. Xia H. Hu X. Li |
| author_sort | Z. Hui |
| collection | DOAJ |
| description | Denoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based on empirical mode decomposition to remove outliers from airborne LiDAR point cloud. Comparing with traditional point cloud denoising algorithms, the proposed method can detect outliers from a signal processing perspective. Firstly, airborne LiDAR point clouds are decomposed into a series of intrinsic mode functions with the help of morphological operations, which would significantly decrease the computational complexity. By applying OTSU algorithm to these intrinsic mode functions, noise-dominant components can be detected and filtered. Finally, outliers are detected automatically by comparing observed elevations and reconstructed elevations. Three datasets located at three different cities in China were used to verify the validity and robustness of the proposed method. The experimental results demonstrate that the proposed method removes both high and low outliers effectively with various terrain features while preserving useful ground details. |
| first_indexed | 2024-12-14T01:00:34Z |
| format | Article |
| id | doaj.art-b99570bc3e204dbd9769bf3aba9cb3b7 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-14T01:00:34Z |
| publishDate | 2019-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-b99570bc3e204dbd9769bf3aba9cb3b72022-12-21T23:23:18ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-06-01XLII-2-W131021102510.5194/isprs-archives-XLII-2-W13-1021-2019A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITIONZ. Hui0P. Cheng1L. Wang2Y. Xia3H. Hu4X. Li5Faculty of Geomatics, East China University of Technology, Nanchang, ChinaFaculty of Geomatics, East China University of Technology, Nanchang, ChinaFaculty of Geomatics, East China University of Technology, Nanchang, ChinaFaculty of Geomatics, East China University of Technology, Nanchang, ChinaFaculty of Geomatics, East China University of Technology, Nanchang, ChinaFaculty of Geomatics, East China University of Technology, Nanchang, ChinaDenoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based on empirical mode decomposition to remove outliers from airborne LiDAR point cloud. Comparing with traditional point cloud denoising algorithms, the proposed method can detect outliers from a signal processing perspective. Firstly, airborne LiDAR point clouds are decomposed into a series of intrinsic mode functions with the help of morphological operations, which would significantly decrease the computational complexity. By applying OTSU algorithm to these intrinsic mode functions, noise-dominant components can be detected and filtered. Finally, outliers are detected automatically by comparing observed elevations and reconstructed elevations. Three datasets located at three different cities in China were used to verify the validity and robustness of the proposed method. The experimental results demonstrate that the proposed method removes both high and low outliers effectively with various terrain features while preserving useful ground details.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1021/2019/isprs-archives-XLII-2-W13-1021-2019.pdf |
| spellingShingle | Z. Hui P. Cheng L. Wang Y. Xia H. Hu X. Li A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION |
| title_full | A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION |
| title_fullStr | A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION |
| title_full_unstemmed | A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION |
| title_short | A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION |
| title_sort | novel denoising algorithm for airborne lidar point cloud based on empirical mode decomposition |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1021/2019/isprs-archives-XLII-2-W13-1021-2019.pdf |
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