Synthetic Image Generation of Shallow Waters Using a Parallelized Hyperspectral Monte Carlo &amp; Analytical Radiative Transfer Model

Bostater, Charles R.; Chiang, Gen-Tao; Huddleston, Lisa L.; Gimond, Manuel

doi:10.1117/12.464377

Cited by 8 publications

(10 citation statements)

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“…The spectral models are then used as input to a FORTRAN 90 radiative transfer simulation program developed and described by Bostater, et al 17,18,19 . The spectral wave models are used to simulate the water surface gravity wave fields as shown below.…”

Section: Surface Water Gravity Wave Simulation and Modeling Methodsmentioning

confidence: 99%

“…Analysis of the gravity wave patch simulations provide wave height and wavelength based spectrums for model calibration and validation to different shallow waters along a changing bathymetric profile. Additional details concerning the wave simulation procedures for any spectral wave model has been published by Bostater, et al 17,18,19 . Excellent additional reviews on water wave simulation techniques and methods have been published in course notes 20 …”

Section: Shallow Water Wave Model Coefficients For Banana River Locationmentioning

confidence: 98%

“…Conceptual schematic (a) showing the FORTRAN 90 hyperspectral Monte Carlo model17,18,19 approach wherein numerically tagged photons enter a simulated water surface facet (b) obtained from simulating a shallow water small gravity wave field depicted in (c). Video images such as (d) are used to create the small gravity wave model coefficients.…”

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confidence: 99%

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Shallow water surface gravity wave imaging, spectra and their use in shallow water dredging operations

Bostater

Yang

2014

SPIE Proceedings

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Imaging of shallow waters using high resolution video imagery is described. Common to mono, stereo and trinocular imaging approaches from ground and airborne platforms is the need to validate the surface water wave field measurements, particularly the amplitude and specular reflectance of water surface small gravity waves. A technique for calibration and validation of water surface gravity wave field energy spectra is described. Results demonstrate the value of video imagery where water level staff gauges with approximately with 0.5 cm wave height accuracy are easily sensed using high definition videography. Essentially, a staff gauge placed in shallow water constructed from PVC materials with custom colored line coding are imaged at 30 H or high frame rates, followed by frame by frame analyses in order to detect the water level measured at 0.5 cm height intervals. The image based time series allow the development of shallow water gravity wave energy spectra using standard FFT analysis procedures. Spectral models based upon peak frequency, for example, are then used in a two dimensional water surface wave simulation model that generates radiative transfer based hyperspectral images of the water surface wave field. The simulated and observed water surface wave patch fields are compared by extracting vertical or horizontal transects within observed and simulated imagery. The approach allows one to developed spectral energy model probability distributions at low cost. The novel noncontact video sensing and image analysis methodology used to calibrate and validate shallow water gravity wave models yield a means for ultimately calculating bottom boundary velocities under measured or simulated wave fields. These boundary layer velocities can cause migration and horizontal particle fluxes (g cm -2 s -1 ), resuspension, settling, and increased turbidity during dredging operations, but not necessarily due to waterway dredging operations and activities.

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Section: Surface Water Gravity Wave Simulation and Modeling Methodsmentioning

confidence: 99%

Section: Shallow Water Wave Model Coefficients For Banana River Locationmentioning

confidence: 98%

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Shallow water surface gravity wave imaging, spectra and their use in shallow water dredging operations

Bostater

Yang

2014

SPIE Proceedings

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“…The model was first developed by Bostater and Gimond (2002) for a water column and Bostater and Chiang (2002) utilized the model to generate realistic synthetic images, and is divided into 5 steps:…”

Section: Hyperspectral Monte Carlo Modelmentioning

confidence: 99%

A pixel to pixel hyperspectral synthetic image model inter-comparison study

Bostater

Bassetti

2006

SPIE Proceedings

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The purpose of this paper is to present simulation results comparing e a Monte Carlo Hyperspectral Simulation Model (MCHSIM) which generates synthetic images with realistic water wave surface to an iterative layered radiative transfer model used to generate hyperspectral synthetic images with realistic water wave surfaces. The Monte Carlo model is divided into 5 steps: (1) generation of the photons, (2) tracking of the photon optical path and simultaneously (3) recording of the photon's location within the water column, (4) a tabulation of the photon location or positions, and conversion to meaningful radiometric quantities and (5) a calculation and processing of the event probabilities between successive photons. This model will was compared to the ILRT which is analytical and uses an iterative method to converge on the solution to the layered two flow radiative transfer model. Overall, the purpose of this research is to develop a better scientific understanding of the influence of the air-sea interface on the remote sensing signal from 400 to 750 nm, as well as the coupled influence of water waves and shallow bottom reflectance effects from benthic aquatic habitat features such as submerged vegetation, corals, and other objects submerged within the water column. Results suggest that the two models are comparing quite well when pixel reflectance data are compared as shown in the 2 dimensional plots. Additional model simulations and comparisons are needed in order for the research to result in practical applications for algorithm development of coastal water quality indicators.

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“…The monte carlo hyperspectral synthetic remote sensing model (MCHSIM) is used to produce images using reflectance of the water surface and these synthetic images are compared to an actual aerial photo obtained from an aircraft in order to improve the accuracy of the numerical model 1 . The MCHSIM is used to simulate a photon at any time step, and then examining the results of the photon tracks or the displacement of the photons as they pass through the water column in a three-dimensional coordinate system.…”

Section: Introductionmentioning

confidence: 99%

A grid enabled Monte Carlo hyperspectral synthetic image remote sensing model (GRID-MCHSIM) for coastal water quality algorithm

et al. 2006

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Previous studies indicate that parallel computing for hyperspectral remote sensing image generation is feasible. However, due to the limitation of computing ability within single cluster, one can only generate three bands and a 1000*1000 pixels image in a reasonable time. In this paper, we discuss the capability of using Grid computing where the so-called eScience or cyberinfrastructure is utilized to integrate distributed computing resources to act as a single virtual computer with huge computational abilities and storage spaces. The technique demonstrated in this paper demonstrates the feasibility of a Grid-Enabled Monte Carlo Hyperspectral Synthetic Image Remote Sensing Model (GRID-MCHSIM) for coastal water quality algorithm.

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Synthetic Image Generation of Shallow Waters Using a Parallelized Hyperspectral Monte Carlo & Analytical Radiative Transfer Model

Cited by 8 publications

References 0 publications

Shallow water surface gravity wave imaging, spectra and their use in shallow water dredging operations

Shallow water surface gravity wave imaging, spectra and their use in shallow water dredging operations

A pixel to pixel hyperspectral synthetic image model inter-comparison study

A grid enabled Monte Carlo hyperspectral synthetic image remote sensing model (GRID-MCHSIM) for coastal water quality algorithm

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