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Готові списки джерел за темами / Lidar equation

Добірка наукової літератури з теми "Lidar equation"

Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 1 лютого 2022

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Статті в журналах з теми "Lidar equation":

1

Bissonnette, Luc R. "Multiple-scattering lidar equation." Applied Optics 35, no. 33 (November 20, 1996): 6449. http://dx.doi.org/10.1364/ao.35.006449.

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2

Wang, X. K., H. Zhao, H. L. Zhang, Y. P. Liu, and C. Shu. "RESEARCH ON INVERSION OF LIDAR EQUATION BASED ON NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 171–76. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-171-2019.

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Abstract. Lidar is an advanced atmospheric and meteorological monitoring instrument. The atmospheric aerosol physical parameters can be acquired through inversion of lidar signals. However, traditional methods of solving lidar equations require many assumptions and cannot get accurate analytical solutions. In order to solve this problem, a method of inverting lidar equation using artificial neural network is proposed. This method is based on BP (Back Propagation) artificial neural network, the weights and thresholds of BP artificial neural network is optimized by Genetic Algorithm. The lidar equation inversion prediction model is established. The actual lidar detection signals are inversed using this method, and the results are compared with the traditional method. The result shows that the extinction coefficient and backscattering coefficient inverted by the GA-based BP neural network model are accurate than that inverted by traditional method, the relative error is below 4%. This method can solve the problem of complicated calculation process, as while as providing a new method for the inversion of lidar equations.

3

Balin, Yu S., S. I. Kavkyanov, G. M. Krekov, and I. A. Razenkov. "Noise-proof inversion of lidar equation." Optics Letters 12, no. 1 (January 1, 1987): 13. http://dx.doi.org/10.1364/ol.12.000013.

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4

Gonzalez, Raul. "Recursive technique for inverting the lidar equation." Applied Optics 27, no. 13 (July 1, 1988): 2741. http://dx.doi.org/10.1364/ao.27.002741.

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5

Krekov, G. M., M. M. Krekova, A. Ya Sukhanov, and A. A. Lisenko. "Lidar equation for a broadband optical range." Technical Physics Letters 35, no. 8 (August 2009): 687–90. http://dx.doi.org/10.1134/s1063785009080021.

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6

Josset, Damien, Peng-Wang Zhai, Yongxiang Hu, Jacques Pelon, and Patricia L. Lucker. "Lidar equation for ocean surface and subsurface." Optics Express 18, no. 20 (September 17, 2010): 20862. http://dx.doi.org/10.1364/oe.18.020862.

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7

Young, Stuart A., Mark A. Vaughan, Ralph E. Kuehn, and David M. Winker. "Corrigendum." Journal of Atmospheric and Oceanic Technology 33, no. 8 (August 2016): 1795–98. http://dx.doi.org/10.1175/jtech-d-16-0081.1.

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AbstractAn error in a recent analysis of the sensitivity of retrievals of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) particulate optical properties to errors in various input parameters is described. This error was in the specification of an intermediate variable that was used to write a general equation for the sensitivities to errors in either the renormalization (calibration) factor or in the lidar ratio used in the retrieval, or both. The result of this incorrect substitution (an additional multiplicative factor to the exponent of the particulate transmittance) was then copied to some intermediate equations; the corrected versions of which are presented here. Fortunately, however, all of the final equations for the specific cases of renormalization and lidar ratio errors are correct, as are all of the figures and approximations, because these were derived directly from equations for the specific errors and not from the equation for the general case. All of the other sections, including the uncertainty analyses and the analyses of sensitivities to low signal-to-noise ratios and errors in constrained retrievals, and the presentations of errors and uncertainties in simulated and actual data are unaffected.

8

Mitra, Kunal, and James H. Churnside. "Transient radiative transfer equation applied to oceanographic lidar." Applied Optics 38, no. 6 (February 20, 1999): 889. http://dx.doi.org/10.1364/ao.38.000889.

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9

Böckmann, Ch, and J. Niebsch. "Inverse scattering problems of the nonlinear LIDAR-equation." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 78, S3 (1998): 867–68. http://dx.doi.org/10.1002/zamm.19980781508.

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10

Kim, Minsu. "Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse." Applied Sciences 9, no. 12 (June 15, 2019): 2452. http://dx.doi.org/10.3390/app9122452.

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Анотація:

An airborne lidar simulator creates a lidar point cloud from a simulated lidar system, flight parameters, and the terrain digital elevation model (DEM). At the basic level, the lidar simulator computes the range from a lidar system to the surface of a terrain using the geomatics lidar equation. The simple computation effectively assumes that the beam divergence is zero. If the beam spot is meaningfully large due to the large beam divergence combined with high sensor altitude, then the beam plane with a finite size interacts with a ground target in a realistic and complex manner. The irradiance distribution of a delta-pulse beam plane is defined based on laser pulse radiative transfer. The airborne lidar simulator in this research simulates the interaction between the delta-pulse and a three-dimensional (3D) object and results in a waveform. The waveform will be convoluted using a system response function. The lidar simulator also computes the total propagated uncertainty (TPU). All sources of the uncertainties associated with the position of the lidar point and the detailed geomatics equations to compute TPU are described. The boresighting error analysis and the 3D accuracy assessment are provided as examples of the application using the simulator.

Більше джерел

Дисертації з теми "Lidar equation":

1

Viklund, Johan. "Atmospheric Attenuation for Lidar Systems in Adverse Weather Conditions." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184706.

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In this study, the weather impact on lidar signals has been researched. A lidar system was placed with a target at approximately 90 m and has together with a weather station collected data for about a year before this study. By using the raw detector data from the lidar, the full waveform can be obtained and the amplitude of the return pulse can be calculated. Atmospheric attenuation of lidar signals is often modeled using the lidar equation, which predicts an exponential decrease in energy over the distance. The factor in the exponent is referred to as the extinction coefficient and it is the main property studied in this thesis. By utilizing models for the extinction coefficient under different weather conditions, it is possible to simulate the performance of the lidar. The extinction coefficient was calculated using different empirical models. The empirical models investigated in this thesis are the Kim and Kruse models for known visibility, the Al Naboulsi model for different types of fog with known visibility, the Carbonneau model for known precipitation amount in rainy conditions, and a similar model for snowy conditions. For the case of rain, a physical model was also used, which is derived through Mie theory. The physical model requires a particle size distribution, which is the number of particles of a certain radius per unit volume. A particle size distribution for rain was generated using the Ulbrich raindrop size distribution, using the precipitation amount recorded by the weather station. Particle size distributions for radiation and advection fog were also simulated. The measured attenuation in lidar signals was compared to the predicted attenuation that was calculated using different models for the extinction coefficient in the lidar equation. Generally, the models tend to underestimate the amplitude of the return pulse. This can partially be explained by the assumptions used to derive the lidar equation, which neglects all augmentation of the beam. The visibility models gave more accurate results compared to the precipitation models. This was expected, since visibility is defined as a measure of attenuation and precipitation amount is not. When a lidar signal is emitted, the light will be reflected from optical surfaces within the lidar and cause a pulse to be detected. This pulse is referred to as the zeropulse. In the first couple of meters of the transmission, we expect to see some backscattered light from adverse weather, since the detector has a larger solid angle at shorter distances. This returned light will be combined with the zeropulse and cause it to expand in width. By examining the zeropulse, it was possible to observe a difference between the average zeropulse under some different weather conditions. This leads to the conclusion that it may be possible to extract some information about current weather conditions from the zeropulse data, given that there is little ambient light and snowy weather conditions. By integrating the zeropulse, variations in the shape of the zeropulse could be described by a single value. Then by separating the data into low and high visibility populations, the zeropulse integral could be used to predict the visibility. The conclusion was that the zeropulse integral can accurately predict whether visibility is above or below a threshold value, given that there is little ambient light and the visibility is known to be below 19950 m.

2

Pliutau, Dzianis. "Computer simulation of stand-off LIBS and Raman LIDAR for remote sensing of distant compounds." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002208.

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3

Hedlund, Marcus. "Weather Influence on LiDAR Signals using the Transient Radiative Transfer and LiDAR Equations." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-79945.

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The ongoing development of self driving cars requires accurate measuring devices and the objective of this thesis was to investigate how di↵erent weather will affect one of these devices, known as a LiDAR. A LiDAR uses pulsed laser light to measure the distance to an object. The main goal of this thesis was to solve the transient radiative transfer equation (TRTE) that describes the propagation of radiation in a scattering, absorbing and emitting media. The TRTE was solved in the frequency domain using the discrete ordinate method (DOM) and a matrix formulation. An alternative model to estimate the amplitude of the return pulse is to use the LiDAR equation which describes the attenuation of a laser pulse in a similar way as Beer-Lamberts law. The difference between the models are that the TRTE accounts for multiple scattering whereas the LiDAR equation only accounts for single scattering. This has the effect that the LiDAR equation only models the change in amplitude of the return pulse whereas the TRTE also models the broadening and shift of the pulse. Experiments were performed with a LiDAR in foggy, rainy and clear weather conditions and compared with the theoretical models. The results from the measurements showed how the amplitude of the pulse decreased in denser fog. However, no tendency to a change in pulse shift and pulse width could be seen from the measured data. Additionally, the measurements showed the effect of ambient light and temperature to the LiDAR signal and also that, even after averaging 300 waveforms, noisy data were a problem. The results from the transient radiative transfer equation showed that in a medium with large optical depth the shift and width of the pulse are highly affected. It was also shown that the amplitude of the pulse calculated with the TRTE seemed to better approximate the experimental data in fog than the LiDAR equation.

4

Vydhyanathan, Arun. "EFFECT OF ATMOSPHERIC PARTICULATES ON AIRBORNE LASER SCANNING FOR TERRAIN-REFERENCED NAVIGATION." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1163793662.

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5

Medeiros, Stephen Conroy. "Incorporating Remotely Sensed Data into Coastal Hydrodynamic Models: Parameterization of Surface Roughness and Spatio-Temporal Validation of Inundation Area." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5434.

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This dissertation investigates the use of remotely sensed data in coastal tide and inundation models, specifically how these data could be more effectively integrated into model construction and performance assessment techniques. It includes a review of numerical wetting and drying algorithms, a method for constructing a seamless digital terrain model including the handling of tidal datums, an investigation into the accuracy of land use / land cover (LULC) based surface roughness parameterization schemes, an application of a cutting edge remotely sensed inundation detection method to assess the performance of a tidal model, and a preliminary investigation into using 3-dimensional airborne laser scanning data to parameterize surface roughness. A thorough academic review of wetting and drying algorithms employed by contemporary numerical tidal models was conducted. Since nearly all population centers and valuable property are located in the overland regions of the model domain, the coastal models must adequately describe the inundation physics here. This is accomplished by techniques that generally fall into four categories: Thin film, Element removal, Depth extrapolation, and Negative depth. While nearly all wetting and drying algorithms can be classified as one of the four types, each model is distinct and unique in its actual implementation. The use of spatial elevation data is essential to accurate coastal modeling. Remotely sensed LiDAR is the standard data source for constructing topographic digital terrain models (DTM). Hydrographic soundings provide bathymetric elevation information. These data are combined to form a seamless topobathy surface that is the foundation for distributed coastal models. A three-point inverse distance weighting method was developed in order to account for the spatial variability of bathymetry data referenced to tidal datums. This method was applied to the Tampa Bay region of Florida in order to produce a seamless topobathy DTM. Remotely sensed data also contribute to the parameterization of surface roughness. It is used to develop land use / land cover (LULC) data that is in turn used to specify spatially distributed bottom friction and aerodynamic roughness parameters across the model domain. However, these parameters are continuous variables that are a function of the size, shape and density of the terrain and above-ground obstacles. By using LULC data, much of the variation specific to local areas is generalized due to the categorical nature of the data. This was tested by comparing surface roughness parameters computed based on field measurements to those assigned by LULC data at 24 sites across Florida. Using a t-test to quantify the comparison, it was proven that the parameterizations are significantly different. Taking the field measured parameters as ground truth, it is evident that parameterizing surface roughness based on LULC data is deficient. In addition to providing input parameters, remotely sensed data can also be used to assess the performance of coastal models. Traditional methods of model performance testing include harmonic resynthesis of tidal constituents, water level time series analysis, and comparison to measured high water marks. A new performance assessment that measures a model's ability to predict the extent of inundation was applied to a northern Gulf of Mexico tidal model. The new method, termed the synergetic method, is based on detecting inundation area at specific points in time using satellite imagery. This detected inundation area is compared to that predicted by a time-synchronized tidal model to assess the performance of model in this respect. It was shown that the synergetic method produces performance metrics that corroborate the results of traditional methods and is useful in assessing the performance of tidal and storm surge models. It was also shown that the subject tidal model is capable of correctly classifying pixels as wet or dry on over 85% of the sample areas. Lastly, since it has been shown that parameterizing surface roughness using LULC data is deficient, progress toward a new parameterization scheme based on 3-dimensional LiDAR point cloud data is presented. By computing statistics for the entire point cloud along with the implementation of moving window and polynomial fit approaches, empirical relationships were determined that allow the point cloud to estimate surface roughness parameters. A multi-variate regression approach was chosen to investigate the relationship(s) between the predictor variables (LiDAR statistics) and the response variables (surface roughness parameters). It was shown that the empirical fit is weak when comparing the surface roughness parameters to the LiDAR data. The fit was improved by comparing the LiDAR to the more directly measured source terms of the equations used to compute the surface roughness parameters. Future work will involve using these empirical relationships to parameterize a model in the northern Gulf of Mexico and comparing the hydrodynamic results to those of the same model parameterized using contemporary methods. In conclusion, through the work presented herein, it was demonstrated that incorporating remotely sensed data into coastal models provides many benefits including more accurate topobathy descriptions, the potential to provide more accurate surface roughness parameterizations, and more insightful performance assessments. All of these conclusions were achieved using data that is readily available to the scientific community and, with the exception of the Synthetic Aperture Radar (SAR) from the Radarsat-1 project used in the inundation detection method, are available free of charge. Airborne LiDAR data are extremely rich sources of information about the terrain that can be exploited in the context of coastal modeling. The data can be used to construct digital terrain models (DTMs), assist in the analysis of satellite remote sensing data, and describe the roughness of the landscape thereby maximizing the cost effectiveness of the data acquisition.
ID: 031001547; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Adviser: Scott C. Hagen.; Co-adviser: John F. Weishampel.; Title from PDF title page (viewed August 23, 2013).; Thesis (Ph.D.)--University of Central Florida, 2012.; Includes bibliographical references.
Ph.D.
Doctorate
Civil, Environmental and Construction Engineering
Engineering and Computer Science
Civil Engineering

Книги з теми "Lidar equation":

1

Zuev, V. E. Inverse Problems of Lidar Sensing of the Atmosphere. Zuev V E, 2013.

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Частини книг з теми "Lidar equation":

1

McManamon, Paul F. "LiDAR Range Equation." In LiDAR Technologies and Systems. SPIE, 2019. http://dx.doi.org/10.1117/3.2518254.ch3.

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2

"Analytical Solutions of the Lidar Equation." In Elastic Lidar, 143–83. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471643173.ch5.

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3

"Essentials and Issues in Separating the Backscatter and Transmission Terms in the Lidar Equation." In Solutions in Lidar Profiling of the Atmosphere, 78–187. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781118963296.ch2.

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4

Williamson, Timothy. "Consequences of the Suppositional Rule." In Suppose and Tell, 31–67. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198860662.003.0003.

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This chapter argues that the Suppositional Rule is a fallible heuristic, because it has inconsistent consequences. They arise in several ways: (i) it implies standard natural deduction rules for ‘if’, and analogous but incompatible rules for refutation in place of proof; (ii) it implies the equation of the probability of ‘If A, C’ with the conditional probability of C on A, which is subject to the trivialization results of David Lewis and others; (iii) its application to complex attitudes generates further inconsistencies. The Suppositional Rule is compared to inconsistent principles built into other linguistic practices: disquotation for ‘true’ and ‘false’ generate Liar-like paradoxes; tolerance principles for vague expressions generate sorites paradoxes. Their status as fallible, semantically invalid but mostly reliable heuristics is not immediately available to competent speakers.

Тези доповідей конференцій з теми "Lidar equation":

1

Liu, Jinbo, Sining Li, Qian Wang, Huizi Li, Qi Wang, and Yuhao Guang. "Lidar Equation Modification for Large Field of View Scannerless Lidar." In 2006 International Workshop on Laser and Fiber-Optical Networks Modeling. IEEE, 2006. http://dx.doi.org/10.1109/lfnm.2006.251983.

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2

Wang, Qi, Jingsong Wei, Jianfeng Sun, and Jian Gao. "Non-scanning imaging laser Lidar equation." In 2012 International Conference on Optoelectronics and Microelectronics (ICOM). IEEE, 2012. http://dx.doi.org/10.1109/icoom.2012.6316256.

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3

Falk, Fritz, and Robert Lange. "Solution method for the lidar equation." In Environmental Sensing '92, edited by Richard J. Becherer and Christian Werner. SPIE, 1992. http://dx.doi.org/10.1117/12.138540.

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4

Samoilova, Svetlana V. "An approximate equation for multiple scattering of spaceborne lidar returns and its application for the retrieval of extinction and depolarization." In Lidar Multiple Scattering Experiments, edited by Christian Werner, Ulrich G. Oppel, and Tom Rother. SPIE, 2003. http://dx.doi.org/10.1117/12.512343.

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5

Kaul, Bruno V. "Lidar equation for sensing optically anisotropic media." In Remote Sensing, edited by Jaqueline E. Russell. SPIE, 1998. http://dx.doi.org/10.1117/12.332687.

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6

Amaral, Marcello M., Marcus P. Raele, Eduardo Landulfo, Silvia Cristina Nunez, Gustavo S. M. Campos, Nilson D. Vieira, Jr., Niklaus U. Wetter, and Anderson Z. Freitas. "Lidar-like equation model for optical coherence tomography signal solution." In SPIE BiOS, edited by Adam P. Wax and Vadim Backman. SPIE, 2011. http://dx.doi.org/10.1117/12.875572.

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7

Xiuyu, Ren, Tian Zhaoshuo, Cui Zihao, Xu Tianchi, Liu Libao, Yang Junguo, Wang Jing, and Fu Shiyou. "Analysis of Brillouin LIDAR equation and maximum detection depth in ocean telemetry." In 2011 Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT). IEEE, 2011. http://dx.doi.org/10.1109/aismot.2011.6159326.

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8

Zhang, Yidong, and Xiangnong Wu. "Study on the rate equation of semiconductor laser in LiDAR detection system." In 2017 16th International Conference on Optical Communications and Networks (ICOCN). IEEE, 2017. http://dx.doi.org/10.1109/icocn.2017.8121185.

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9

Harsdorf, Stefan, and Rainer Reuter. "Laser remote sensing in highly turbid waters: validity of the lidar equation." In Industrial Lasers and Inspection (EUROPTO Series), edited by Michel R. Carleer, Moira Hilton, Torsten Lamp, Rainer Reuter, George M. Russwurm, Klaus Schaefer, Konradin Weber, Klaus C. H. Weitkamp, Jean-Pierre Wolf, and Ljuba Woppowa. SPIE, 1999. http://dx.doi.org/10.1117/12.364200.

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10

Veretennikov, Victor V. "Lidar equation in the second-order approximation for media with a strongly extended phase function." In Eighth Joint International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, edited by Gelii A. Zherebtsov, Gennadii G. Matvienko, Viktor A. Banakh, and Vladimir V. Koshelev. SPIE, 2002. http://dx.doi.org/10.1117/12.458448.

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