An automated statistical analysis approach to noise reduction for photon-counting lidar systems

Horan, K. H.; Kerekes, John P.

doi:10.1109/igarss.2013.6723794

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…The main purpose of the preprocessing is to decrease the number of background photons and to reduce the amount of calculation needed in the subsequent steps. Image processing [25,28,29], envelope curves [26], and histogram statistics [26,[30][31][32][33] are the commonly used methods in the preprocessing step. 1) Image processing: The first preprocessing method involves rasterizing the profile point cloud along the orbit into a 2D image according to the density of the photons.…”

Section: A Preprocessingmentioning

confidence: 99%

“…In some studies, this method has also been directly used to detect the surface, without other subsequent steps. In 2013, Horan et al [33] divided the photons into along-track distance segments containing the same number of photons, and counted the number of photons in each specific elevation range bin. In this method, the photons within an elevation threshold are considered as surface photons, and the elevation threshold is set to a certain elevation range above and below the elevation corresponding to the maximum number of photons.…”

Section: Subsequent Processingmentioning

confidence: 99%

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A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

Xie

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) was launched on September 15, 2018. It is the first photon-counting laser altimeter satellite, which is of great significance for the research into laser altimetry. ICESat-2 is, however, highly sensitive and susceptible to environmental influences. In addition to surface returns, a lot of non-surface 1 photons are found in the data. It is therefore necessary to study an effective method to separate the surface signal from background information. In this paper, we review the existing surface detection methods for photon point cloud data and select seven methods for comparison. Four sources of photon-counting data were considered in the experiments: The Multiple Altimeter Beam Experimental Lidar(MABEL), the Chinese Multi-Beam LiDAR(MBL), The Advanced Topographic Laser Altimeter System(ICESat-2/ATLAS), and MATLAS(using MABEL data to simulate the expected ATLAS photon point cloud). Four scenarios of land, land ice, sea ice, and ocean were also considered. Each surface detection method was tested in 12 experiments, and the different methods were finally compared by qualitative and quantitative measures. We were thus able to establish the advantages and disadvantages of each method, which will be of great significance for scholars studying surface detection methods.

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Section: A Preprocessingmentioning

confidence: 99%

Section: Subsequent Processingmentioning

confidence: 99%

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

Xie

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Photon-counting technology provides many advantages over conventional linear-mode LiDAR systems [11] but also introduces new challenges for discriminating true returns from the photon-counting point cloud due to a significant number of solar noise photons and system dark current returns [12]. Therefore, an effective approach is necessary to filter the photon-counting data and extract accurate topographic models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Noise Filtering Model for Photon-Counting Laser Altimeter Data

Wang

Pan

Glennie

2016

IEEE Geosci. Remote Sensing Lett.

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The new generation of Ice, Cloud, and land Elevation Satellite (ICESat-2) which utilizes photon-counting laser detectors is scheduled for launch in 2017. This upcoming mission will provide data to assess changes of ice sheet elevation and mass, as well as the time-varying volume of sea ice. However, the next-generation ICESat sensor also presents new data processing challenges due to the high number of false returns present in the resultant point cloud that are mainly caused by the high sensitivity of the photon detector to solar returns. In this letter, we propose a novel noise filter for single photon laser altimeter data utilizing a Bayesian decision theory. We applied our algorithm to the Multiple Altimeter Beam Experimental Lidar (MABEL) data sets and compared the filtered estimate of ground to coincident high resolution airborne LiDAR data. The results show that the proposed algorithm differentiates between noise and ground surface returns effectively with 6-m root-mean-square error (RMSE) for the MABEL green channel lasers and 4-m RMSE for the near-infrared channel lasers. The Bayesian approach also outperformed a commonly applied point density-based algorithm, the modified density-based spatial clustering of applications with noise, particularly in areas of steep terrain. Index Terms-Bayesian, Ice, Cloud, and land Elevation Satellite (ICESat), kth nearest neighbor (KNN), Multiple Altimeter Beam Experimental Lidar (MABEL), single photon counting.

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“…Previous research has demonstrated noise filtering techniques for simulated ICESat-2 [12] and MABEL data [13]. In this letter, a clustering method is modified and used for the detection of the ground surface and vegetation canopy in photon-counting laser altimeter data.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Density-Based Model for Extracting Surface Returns From Photon-Counting Laser Altimeter Data

Zhang

Kerekes

2015

IEEE Geosci. Remote Sensing Lett.

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The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission of the National Aeronautics and Space Administration is scheduled to launch in 2017. This upcoming mission aims to provide data to determine the temporal and spatial changes of ice sheet elevation, sea ice freeboard, and vegetation canopy height. A photon-counting lidar onboard ICESat-2 yields point clouds resulting from surface returns and noise. In support of the ICESat-2 mission, this letter derives an adaptive density-based model that is capable of detecting the ground surface and vegetation canopy in photon-counting laser altimeter data. Based on results from point clouds generated by a first principle simulation and those observed by the Multiple Altimeter Beam Experimental Lidar, the ground and canopy returns can be reliably extracted using the proposed approach. Further study on performance assessment shows that smoother surfaces will result in improved accuracy of ground height estimation. In addition, the proposed detection approach has better performance in environments with lower noise, although the performance evaluation metric F -measure does not vary significantly over a range of noise rates (0.5-5 MHz). This proposed approach is generally applicable for surface and canopy finding from photon-counting laser altimeter data.Index Terms-Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ice, Cloud and land Elevation Satellite-2 (ICESat-2), lidar, surface finding.

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An automated statistical analysis approach to noise reduction for photon-counting lidar systems

Cited by 12 publications

References 9 publications

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data

A Novel Noise Filtering Model for Photon-Counting Laser Altimeter Data

An Adaptive Density-Based Model for Extracting Surface Returns From Photon-Counting Laser Altimeter Data

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