Building LiDAR point cloud denoising processing through sparse representation

Xie Bingqian; Gu Yanfeng; Cao Zhimin

doi:10.1109/IGARSS.2015.7325831

Building LiDAR point cloud denoising processing through sparse representation

Xie Bingqian
, Gu Yanfeng
, Cao Zhimin

School of Electronics and Information Engineering

Harbin Institute of Technology
North-East Petroleum University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Nowdays, airborne LiDAR comes into a popular way to survey the ground scene, particularly for the application of building reconstruction. However, the LiDAR point cloud acquired is usually polluted by noise for the existence of LiDAR system's inherent error and aircraft's shock. Thus, before LiDAR data is used, a preprocessing such as denoising is needed. This paper focus on the denoising of building LiDAR data. First, the building LiDAR point cloud is rasterized into a two- dimensional image. Then, a dictionary learned from training samples is used to denoise the image according to signal's sparse representation theory. Last, we can get the building's raster image with little noise.

Original language	English
Title of host publication	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	585-588
Number of pages	4
ISBN (Electronic)	9781479979295
DOIs	https://doi.org/10.1109/IGARSS.2015.7325831
State	Published - 10 Nov 2015
Event	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2015-November

Conference

Conference	IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/Territory	Italy
City	Milan
Period	26/07/15 → 31/07/15

Keywords

LiDAR point cloud
building
denoising
sparse representation

Access to Document

10.1109/IGARSS.2015.7325831

Cite this

Bingqian, X., Yanfeng, G., & Zhimin, C. (2015). Building LiDAR point cloud denoising processing through sparse representation. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings (pp. 585-588). Article 7325831 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2015.7325831

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title = "Building LiDAR point cloud denoising processing through sparse representation",

abstract = "Nowdays, airborne LiDAR comes into a popular way to survey the ground scene, particularly for the application of building reconstruction. However, the LiDAR point cloud acquired is usually polluted by noise for the existence of LiDAR system's inherent error and aircraft's shock. Thus, before LiDAR data is used, a preprocessing such as denoising is needed. This paper focus on the denoising of building LiDAR data. First, the building LiDAR point cloud is rasterized into a two- dimensional image. Then, a dictionary learned from training samples is used to denoise the image according to signal's sparse representation theory. Last, we can get the building's raster image with little noise.",

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Bingqian, X, Yanfeng, G & Zhimin, C 2015, Building LiDAR point cloud denoising processing through sparse representation. in 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings., 7325831, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 585-588, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7325831

Building LiDAR point cloud denoising processing through sparse representation. / Bingqian, Xie; Yanfeng, Gu; Zhimin, Cao.
2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 585-588 7325831 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Nowdays, airborne LiDAR comes into a popular way to survey the ground scene, particularly for the application of building reconstruction. However, the LiDAR point cloud acquired is usually polluted by noise for the existence of LiDAR system's inherent error and aircraft's shock. Thus, before LiDAR data is used, a preprocessing such as denoising is needed. This paper focus on the denoising of building LiDAR data. First, the building LiDAR point cloud is rasterized into a two- dimensional image. Then, a dictionary learned from training samples is used to denoise the image according to signal's sparse representation theory. Last, we can get the building's raster image with little noise.

AB - Nowdays, airborne LiDAR comes into a popular way to survey the ground scene, particularly for the application of building reconstruction. However, the LiDAR point cloud acquired is usually polluted by noise for the existence of LiDAR system's inherent error and aircraft's shock. Thus, before LiDAR data is used, a preprocessing such as denoising is needed. This paper focus on the denoising of building LiDAR data. First, the building LiDAR point cloud is rasterized into a two- dimensional image. Then, a dictionary learned from training samples is used to denoise the image according to signal's sparse representation theory. Last, we can get the building's raster image with little noise.

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Bingqian X, Yanfeng G, Zhimin C. Building LiDAR point cloud denoising processing through sparse representation. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 585-588. 7325831. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2015.7325831

Building LiDAR point cloud denoising processing through sparse representation

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Keywords

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