A novel nonparametric adaptive kernel density estimation method for removing nonrandom noise from ICESat-2 photon-counting LiDAR data.
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| Title: | A novel nonparametric adaptive kernel density estimation method for removing nonrandom noise from ICESat-2 photon-counting LiDAR data. |
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| Authors: | Wang, Zijia1,2,3 (AUTHOR), Nie, Sheng1,2,3 (AUTHOR) niesheng@aircas.ac.cn, Wang, Cheng1,2,3 (AUTHOR), Xi, Xiaohuan1,2 (AUTHOR), Zhu, Xiaoxiao4 (AUTHOR), Lao, Jieying5 (AUTHOR), Yang, Bisheng6 (AUTHOR) |
| Source: | Geo-Spatial Information Science. Apr2026, Vol. 29 Issue 2, p1177-1195. 19p. |
| Subjects: | Multiple scattering (Physics), Photon counting, Principal components analysis, Artificial satellites, Probability density function, Noise control, Altitude measurements, Optical reflection |
| Abstract: | The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) demonstrates significant advantages in retrieving surface elevation. However, its unique photon-counting detection mechanism and vertical profiling introduce substantial noise, particularly nonrandom noise from multiple scattering and specular reflections. These two noise types often coexist in large-scale regions, yet most existing parameterized algorithms are designed to address only one, limiting their denoising effectiveness in complex environments. To address this challenge, a robust nonparametric density estimation algorithm is proposed to efficiently remove nonrandom noise while ensuring accurate surface elevation retrieval. The proposed method adaptively determines the K nearest neighbors based on the photon distribution and employs Principal Component Analysis (PCA) to identify the orientation and axes of a search ellipse for adaptive bandwidth estimation. Local deviation factors are then computed, followed by adaptive thresholding along the track distance to extract high-quality signal photons. The proposed algorithm is tested on ICESat-2 datasets acquired under varying laser intensity levels, including glaciers characterized by multiple scattering, inland water affected by specular reflection, and sea ice where both types of nonrandom noise coexist. Experimental results demonstrate superior performance across diverse surface types, achieving an average F (the harmonic mean of Recall and Precision) of 0.9896. Elevation profiles fitted by our method closely align with manually labeled results, exhibiting an average bias of 0.003 m, an average MAE of 0.006 m, and an average RMSE of 0.010 m, outperforming the compared methods. Specifically, our method reduces the mean bias by approximately 86.96% compared to the specular return removal method, 57.14% compared to the multiple scattering removal method, and 95.00% compared to ICESat-2 data products. In conclusion, this study provides an effective and generalizable solution for eliminating nonrandom noise in highly reflective or scattering environments, demonstrating robust performance and high accuracy across diverse surface types and large-scale scene data. [ABSTRACT FROM AUTHOR] |
| Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195126709 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel nonparametric adaptive kernel density estimation method for removing nonrandom noise from ICESat-2 photon-counting LiDAR data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zijia%22">Wang, Zijia</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nie%2C+Sheng%22">Nie, Sheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> niesheng@aircas.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Cheng%22">Wang, Cheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xi%2C+Xiaohuan%22">Xi, Xiaohuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Xiaoxiao%22">Zhu, Xiaoxiao</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lao%2C+Jieying%22">Lao, Jieying</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Bisheng%22">Yang, Bisheng</searchLink><relatesTo>6</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Geo-Spatial+Information+Science%22">Geo-Spatial Information Science</searchLink>. Apr2026, Vol. 29 Issue 2, p1177-1195. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+scattering+%28Physics%29%22">Multiple scattering (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Photon+counting%22">Photon counting</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Noise+control%22">Noise control</searchLink><br /><searchLink fieldCode="DE" term="%22Altitude+measurements%22">Altitude measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+reflection%22">Optical reflection</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) demonstrates significant advantages in retrieving surface elevation. However, its unique photon-counting detection mechanism and vertical profiling introduce substantial noise, particularly nonrandom noise from multiple scattering and specular reflections. These two noise types often coexist in large-scale regions, yet most existing parameterized algorithms are designed to address only one, limiting their denoising effectiveness in complex environments. To address this challenge, a robust nonparametric density estimation algorithm is proposed to efficiently remove nonrandom noise while ensuring accurate surface elevation retrieval. The proposed method adaptively determines the K nearest neighbors based on the photon distribution and employs Principal Component Analysis (PCA) to identify the orientation and axes of a search ellipse for adaptive bandwidth estimation. Local deviation factors are then computed, followed by adaptive thresholding along the track distance to extract high-quality signal photons. The proposed algorithm is tested on ICESat-2 datasets acquired under varying laser intensity levels, including glaciers characterized by multiple scattering, inland water affected by specular reflection, and sea ice where both types of nonrandom noise coexist. Experimental results demonstrate superior performance across diverse surface types, achieving an average F (the harmonic mean of Recall and Precision) of 0.9896. Elevation profiles fitted by our method closely align with manually labeled results, exhibiting an average bias of 0.003 m, an average MAE of 0.006 m, and an average RMSE of 0.010 m, outperforming the compared methods. Specifically, our method reduces the mean bias by approximately 86.96% compared to the specular return removal method, 57.14% compared to the multiple scattering removal method, and 95.00% compared to ICESat-2 data products. In conclusion, this study provides an effective and generalizable solution for eliminating nonrandom noise in highly reflective or scattering environments, demonstrating robust performance and high accuracy across diverse surface types and large-scale scene data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Geo-Spatial Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10095020.2025.2539952 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1177 Subjects: – SubjectFull: Multiple scattering (Physics) Type: general – SubjectFull: Photon counting Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Artificial satellites Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Noise control Type: general – SubjectFull: Altitude measurements Type: general – SubjectFull: Optical reflection Type: general Titles: – TitleFull: A novel nonparametric adaptive kernel density estimation method for removing nonrandom noise from ICESat-2 photon-counting LiDAR data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Zijia – PersonEntity: Name: NameFull: Nie, Sheng – PersonEntity: Name: NameFull: Wang, Cheng – PersonEntity: Name: NameFull: Xi, Xiaohuan – PersonEntity: Name: NameFull: Zhu, Xiaoxiao – PersonEntity: Name: NameFull: Lao, Jieying – PersonEntity: Name: NameFull: Yang, Bisheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10095020 Numbering: – Type: volume Value: 29 – Type: issue Value: 2 Titles: – TitleFull: Geo-Spatial Information Science Type: main |
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