What optech&amp;#x2019;s bathymetric LiDAR sees underwater

Long, Bernard; Cottin, Antoine; Collin, Antoine

doi:10.1109/igarss.2007.4423518

Cited by 13 publications

(8 citation statements)

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“…Decomposition of lidar waveforms using Gaussian mixture models and the EM algorithm has been previously demonstrated and performs adequately for our purpose [31]. The maximum-elevation mode output (given sufficient weight) from the EM GM algorithm is taken as the mean sea surface level [28].…”

Section: Bathymetric Dtm Generationmentioning

confidence: 99%

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Cossio

Slatton

et al. 2010

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

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Recent technological advances in the performance of small micro-lasers and multi-channel multi-event photo-detectors have enabled the development of experimental airborne lidar (light detection and ranging) systems based on a low-SNR (LSNR) paradigm. Due to dense point spacing (tens of points per square meter) and sub-decimeter range resolution, LSNR lidar can likely enable detection of meter-scale targets that would go unnoticed by traditional lidar technology. Small vehicle obstructions and other similar targets in the beach and littoral zones are of particular interest, because of LSNR lidar's applicability to the near-shore environment and the general desire to improve detection of antivehicle and antipersonnel obstacles in the coastal zone.A target detection procedure is presented that exploits the detailed information available from LSNR lidar data while diminishing the effect of spurious noise events. Consideration is given to detection in both topographic and bathymetric scenarios. Data sets for target detection analysis are supplied by a numerical sensor simulator developed at the University of Florida. Target detection performance is evaluated as a function of environmental characteristics, such as water clarity and depth, and system parameters, specifically transmitted pulse energy and laser pulse repetition frequency. Analysis of results with regards to consideration for future system design is discussed.

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Section: Bathymetric Dtm Generationmentioning

confidence: 99%

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Cossio

Slatton

et al. 2010

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

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“…Waveforms presenting a detectable bottom are fitted by mathematical functions. Several functions have been performed in the literature to fit the waveform components, such as two Gaussian curves to approximate both the surface and the bottom returns [26] or three Gaussian curves used [68] to fit the water surface, the water column and the bottom return. Other functions are also used, such as the Weibull distribution and the Burr function, to approximate asymmetric waveforms [69].…”

Section: Bathymetry Estimation From Wa-lid Waveformsmentioning

confidence: 99%

Potential of Space-Borne LiDAR Sensors for Global Bathymetry in Coastal and Inland Waters

Abdallah

Bailly

Baghdadi³

et al. 2013

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

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International audienceThis work aimed to prospect future space-borne LiDAR sensor capacities for global bathymetry over inland and coastal waters. The sensor performances were assessed using a methodology based on waveform simulation. A global representative simulated waveform database is first built from the Wa-LiD (Water LiDAR) waveform simulator and from distributions of water parameters assumed to be representative at the global scale. A bathymetry detection and estimation process is thus applied to each waveform to determine the bathymetric measurement probabilities in coastal waters, shallow lakes, deep lakes and rivers for a range of water depths. Finally, with a sensitivity analysis of waveforms, the accuracy and some limiting factors of the bathymetry are identified for the dominant water parameters. Two future space-borne LiDAR sensors were explored: an ultraviolet (UV) LiDAR and a green LiDAR. The results show that the bathymetric measurement probabilities at a 1 m depth is 63%, 54%, 24% and 19% with the green LiDAR for deep lakes, coastal waters, rivers and shallow lakes, respectively, and 10%, 22%, 1% and 0% with the UV LiDAR, respectively. The threshold values of dominant water parameters (sediment, yellow substance and phytoplankton concentrations) above which bathymetry detection fails were identified and mapped. The accuracy on the bathymetry estimates for both LiDAR sensors is 2.8 cm for one standard deviation with negligible bias (approximately 0.5 cm). However, these accuracy statistics only include the errors coming from the digitizing resolution and the inversion algorithm

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“…Simon-Miller and Reuter 2013) and used by many lidar systems (e.g. Whiteway et al (2008); Long, Cottin, and Collin (2007); Daly et al (2014)). We also make measurements at 852 nm to determine whether the patterns that we observe at 1064 nm are consistent with those observed at a different near-IR wavelength.…”

Section: Introductionmentioning

confidence: 99%

Reflectance properties of grey-scale Spectralon® as a function of viewing angle, wavelength, and polarization

Shaw

Daly

Cloutis

et al. 2016

International Journal of Remote Sensing

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The remote-sensing community uses grey-scale Spectralon to evaluate the performance of instruments designed to observe geologic surfaces with a range of reflectance values. This article presents the normalized biconical reflectance factor of a series of grey-scale Spectralon targets taken at viewing angles ranging from 10°to 80°. The darkest Spectralon standard, when illuminated at nadir incidence with s-polarized 1064 nm light, first undergoes a decrease in reflectance factor with increasing viewing angle, then encounters a minimum, after which the reflectance factor increases with increasing viewing angle. When progressively brighter Spectralon samples are measured, the reflectance factor dip at lower emergence angles becomes less pronounced and the slope at higher emergence angles is more gradual until, for the 20% Spectralon sample, the curve dips downward at the highest emergence angles. Measuring the grey-scale Spectralon set of targets using p-polarized incident light, the same trends described above are seen, except for the darkest Spectralon target, which monotonically decreases in reflectance factor. The data show a total decrease of 44%. The reflectance factor curves observed at 852 nm are similar to those seen at 1064 nm in that the same number of inflection points and the same sign of slope is seen at both wavelengths. However, comparing two different incidence angles using 1064 nm s polarized incident light, the slope of the reflectance factor curve does change significantly. For the brightest Spectralon target, the slope is positive for i = 60°and negative for i = 0°at all viewing angles measured. For the darkest Spectralon target, the slope is larger for i = 60°than for i = 0°at all viewing angles measured. At i = 60°, the data show a total increase of 1360% in reflectance factor. ARTICLE HISTORY

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What optech’s bathymetric LiDAR sees underwater

Cited by 13 publications

References 0 publications

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Potential of Space-Borne LiDAR Sensors for Global Bathymetry in Coastal and Inland Waters

Reflectance properties of grey-scale Spectralon® as a function of viewing angle, wavelength, and polarization

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