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

Wang, Xiao; Pan, Zhigang; Glennie, C. L.

doi:10.1109/lgrs.2016.2555308

Cited by 54 publications

(33 citation statements)

References 17 publications

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“…(2) Getting the maximum density of each photon. A search ellipse was adopted to calculate the photon density according to previous studies [25,27,38,39]. As the distribution of photons is closely related to the terrain slopes, an ellipse relative to the surface slopes can be generated to achieve better density statistics [5].…”

Section: Noise Photon Removalmentioning

confidence: 99%

“…[24] proposed a modified density-based spatial clustering of applications with noise (mDBSCAN) method. Wang et al [25] applied determination of the probability of the kth nearest neighbor(KNN) and Bayesian decision theory to divide the raw data into signal and noise photons. Popescu et al [26] proposed the grid-based statistical (GBS) filtering method to identify possible signal grid cells and then utilized cluster analysis to remove noise photons.…”

Section: Introductionmentioning

confidence: 99%

“…The voxel-based spatial filtering method is mainly efficient for the single photon LIDAR dataset with a high signal-to-noise ratio (SNR), which may not be suitable for ICESat-2 data with a low SNR [23]. Density clustering-based algorithms usually fail to effectively filter out noise photons in vegetated environments with steep terrain [24][25][26]. Furthermore, the surface spectral reflectance, atmospheric conditions, and solar incidence angle spatially vary in the along-track direction, leading to the inconsistency of photon density, which may also greatly affect the performance of the density clustering-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

et al. 2018

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The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.

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Section: Noise Photon Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

et al. 2018

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“…However, the study data is nighttime photon data, which is less affected by solar background noise, so noise filitering can filiter noise photon data by threshold statistics. Wang et al (2016) filtering method based on SPL data as experimental data, and determined the optimal voxel size for the optimal filter by testing different voxel sizes. It is finally determined that Garrett algorithm is the optimal algorithm for the experimental data, and 3m×3m×0.2m is the optimal voxel size of the test data.…”

Section: Review Of Simulated Photon Lidar Noisementioning

confidence: 99%

Review of Noise Filtering Algorithm for Photon Data

Huang

Xing

Qin

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1)/Geoscience Laser Altimeter System (GLAS), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) , which is equipped with the Advanced Topographic Laser Altimeter (ATLAS) system, was successfully launched in 2018. Since ICESat-1/GLAS has facilitated scientific results in the field of forest structure parameter estimation, how to use the ICESat-2/ATLAS photon cloud data to estimate forest structure parameters has become a hotspot in the field of spaceborne photon data application. However, due to the weak photon characteristics of the ICESat-2/ATLAS system, the system is extremely susceptible to noise, which poses a challenge for its subsequent accurate estimation of forest structural parameters. Aiming to filter out the noise photons, the paper introduces the advantages of the spaceborne lidar system ICESat-2/ATLAS than ICESat-1/GLAS. The paper summarizes the research of the simulated photon-counting lidar (PCL) noise filtering algorithm and noise filtering on spaceborne.

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“…Only with advances in photon-detector ranging resolution and dead time have photon-counting systems become viable mapping tools [40]. A number of recent publications target terrestrial and cryospheric applications, such as canopy extraction [41][42][43] and sea-ice and glacier profiling [44][45][46][47], and noise-filtering techniques [48][49][50], but research pertaining to using photon-counting lidar for bathymetric mapping remains limited.…”

Section: Introductionmentioning

confidence: 99%

Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS

Forfinski-Sarkozi

Parrish

2016

Remote Sensing

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Abstract:In 2018, the National Aeronautics and Space Administration (NASA) is scheduled to launch the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), with a new six-beam, green-wavelength, photon-counting lidar system, Advanced Topographic Laser Altimeter System (ATLAS). The primary objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass. However, if bathymetry can be reliably retrieved from ATLAS data, this could assist in addressing a key data need in many coastal and inland water body areas, including areas that are poorly-mapped and/or difficult to access. Additionally, ATLAS-derived bathymetry could be used to constrain bathymetry derived from complementary data, such as passive, multispectral imagery and synthetic aperture radar (SAR). As an important first step in evaluating the ability to map bathymetry from ATLAS, this study involves a detailed assessment of bathymetry from the Multiple Altimeter Beam Experimental Lidar (MABEL), NASA's airborne ICESat-2 simulator, flown on the Earth Resources 2 (ER-2) high-altitude aircraft. An interactive, web interface, MABEL Viewer, was developed and used to identify bottom returns in Keweenaw Bay, Lake Superior. After applying corrections for refraction and channel-specific elevation biases, MABEL bathymetry was compared against National Oceanic and Atmospheric Administration (NOAA) data acquired two years earlier.The results indicate that MABEL reliably detected bathymetry in depths of up to 8 m, with a root mean square (RMS) difference of 0.7 m, with respect to the reference data. Additionally, a version of the lidar equation was developed for predicting bottom-return signal levels in MABEL and tested using the Keweenaw Bay data. Future work will entail extending these results to ATLAS, as the technical specifications of the sensor become available.

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

Cited by 54 publications

References 17 publications

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

Review of Noise Filtering Algorithm for Photon Data

Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS

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