Bayesian Network Modeling of Imagery Features

Bayesian Network Modeling of Imagery Features From Direct Numerically Simulated Turbulent Sediment-Laden Oscillatory Flow

Presented at the 6th Annual BayesiaLab Conference in Chicago, November 1-2, 2018.


Direct numerically simulated data can serve as a proxy for understanding many issues concerning multidimensional remotely sensed data. As a step towards performing operational Bayesian belief network modeling for rivers, which is of practical utility to naval intelligence, direct numerically simulated sediment-laden oscillatory flow is used to estimate statistical surface layer spatial eddy scales. This is done using spatial realizations of the sediment concentration, vertical velocity, and pressure fields, along with feature extraction algorithms that utilize self-organizing mapping, independent component analysis, and two-dimensional omnidirectional Morlet wavelet analysis. Stress versus scale statistical distributions exhibit distinct phase modulation over the three ambient forcing phases of maximum negative, zero, and maximum positive velocity. The stress versus sediment concentration scale distribution, which is of great pertinence to riverine remote sensing, exhibits a significant amount of large eddy scales, suggesting coherent large-scale sediment structure formation, possibly due to particle interstitial forces. Estimated statistical results, in turn, serve as feature parameters for naïve Bayesian belief network modeling of bottom boundary layer stress and surface eddy scale observations. From a diagnostic reasoning viewpoint, initial results suggest that robustly inferring sub-surface boundary layer stress from surface sediment concentration eddy scales uniquely may be a difficult task. From a prognostic reasoning viewpoint, preliminary model results suggest that large sediment concentration eddy scales may result from the application of large positive Reynolds stress. The model formalism used allows for the ability to statistically characterize flow structure at depth from observations taken across a surface boundary layer. This makes the results relevant to image analysis at the air-sea interfacial boundary layer in large-scale coastal and riverine systems.

Presentation Video


Nicholas V. Scott, Ph.D.
Riverside Research Institute, Dayton Research Center

Tian-Jian Hsu, Ph.D.
University of Delaware, Center for Applied Coastal Research

About the Presenter

Dr. Nicholas Scott has been a member of the professional staff at Riverside Research in Dayton, OH, since October 2012, working predominantly in the area of hyperspectral and multispectral image analysis. He investigates the applicability of traditional and non-traditional signal and image processing techniques to the extraction of information from remotely sensed imagery. His present work includes cognitive modeling of geo-intelligence information and the application of pattern recognition techniques to turbulent flow imagery. He is also involved in the application of probabilistic graphical modeling algorithms for information fusion and statistical inference.

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