Photon-Counting Lidar: An Adaptive Signal Detection Method for Different Land Cover Types in Coastal Areas

Ma, Yi; Zhang, Wenhao; Sun, Jinyan; Li, Guoyuan; Wang, Xiaohua; Li, Song; Xu, Nan

doi:10.3390/rs11040471

Cited by 28 publications

(13 citation statements)

References 33 publications

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“…For different land-covers, many methods were proposed to detect signal photons from raw noisy points [34][35][36][37]. The previous study proposed a DBSCAN method and performed well in the airborne MABEL (Multiple Altimeter Beam Experimental Lidar) raw data points in coastal areas [38]. The MABEL is a technique indicator of ICESat-2 sensor [18], and its sensors and corresponding data exhibit some differences.…”

Section: Detection Of Surface Profiles From Icesat-2 Datamentioning

confidence: 99%

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Zhang

et al. 2020

Remote Sensing

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With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

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Section: Detection Of Surface Profiles From Icesat-2 Datamentioning

confidence: 99%

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Zhang

et al. 2020

Remote Sensing

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“…Because the spacecraft position errors [∆X s , ∆Y s , ∆Z s ] T can be calibrated through the ground observation station, the deviation of X p , Y p , Z p T ITRF from the actual position (X P , Y P , Z P ) T ITRF can be calculated by Formula (4).…”

Section: Pointing Angle Error Geometric Modelmentioning

confidence: 99%

“…The spaceborne laser altimeter, as an important instrument for earth observation, is of great significance for global ecosystem observation and for glacier and lake research [1][2][3][4][5][6]. Following the Geoscience Laser Altimeter System (GLAS) of the Ice, Cloud and Land Elevation Satellite (ICESat) [7], which was a single-beam instrument that recorded the received laser energy as a waveform, the National Aeronautics and Space Administration (NASA) launched the Ice, Cloud and Land Elevation Satellite-2 ICESat-2 satellite on 15 September 2018 [8,9].…”

Section: Introductionmentioning

confidence: 99%

Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

et al. 2019

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The satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) has been equipped with a new type of spaceborne laser altimeter, which has the benefits of having small footprints and a high repetition rate, and it can produce dense footprints on the ground. Focusing on the pointing angle calibration of this new spaceborne laser altimeter, this paper proposes a fast pointing angle calibration method using only a small range of terrain surveyed by airborne lidar. Based on the matching criterion of least elevation difference, an iterative pointing angle calibration method was proposed. In the experiment, the simulated photon-counting laser altimeter data and the Ice, Cloud and Land Elevation Satellite-2 data were used to verify the algorithm. The results show that when 1 km and 2.5 km lengths of track were used, the pointing angle error after calibration could be reduced to about 0.3 arc-seconds and less than 0.1 arc-seconds, respectively. Meanwhile, compared with the traditional pyramid search method, the proposed iterative pointing angle calibration method does not require well-designed parameters, which are important in the pyramid search method to balance calculation time and calibration result, and the iterative pointing angle calibration method could significantly reduce the calibration time to only about one-fifth of that of the pyramid search method.

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“…Benefiting from the excellent photon-efficiency sensitivity and picosecond-level temporal resolution, photon counting lidar equipped with Geiger mode avalanche photodiode (Gm-APD) has been widely used in long-range high-resolution ranging and 3D imaging [1][2][3][4][5][6][7]. Ranging error is the most important index to evaluate the ranging performance of photon counting lidar.…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Anti-Noise Method for Reducing Ranging Error in Photon Counting Lidar

Huang

Zhang

Xie

et al. 2021

Entropy

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Photon counting lidar for long-range detection faces the problem of declining ranging performance caused by background noise. Current anti-noise methods are not robust enough in the case of weak signal and strong background noise, resulting in poor ranging error. In this work, based on the characteristics of the uncertainty of echo signal and noise in photon counting lidar, an entropy-based anti-noise method is proposed to reduce the ranging error under high background noise. Firstly, the photon counting entropy, which is considered as the feature to distinguish signal from noise, is defined to quantify the uncertainty of fluctuation among photon events responding to the Geiger mode avalanche photodiode. Then, the photon counting entropy is combined with a windowing operation to enhance the difference between signal and noise, so as to mitigate the effect of background noise and estimate the time of flight of the laser pulses. Simulation and experimental analysis show that the proposed method improves the anti-noise performance well, and experimental results demonstrate that the proposed method effectively mitigates the effect of background noise to reduce ranging error despite high background noise.

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Photon-Counting Lidar: An Adaptive Signal Detection Method for Different Land Cover Types in Coastal Areas

Cited by 28 publications

References 33 publications

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Iterative Pointing Angle Calibration Method for the Spaceborne Photon-Counting Laser Altimeter Based on Small-Range Terrain Matching

An Entropy-Based Anti-Noise Method for Reducing Ranging Error in Photon Counting Lidar

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