Scene Motion Detection in Imagery with Anisoplanatic Optical Turbulence

Date of Award


Degree Name

Ph.D. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Russell Hardie


In long range imaging applications, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally-varying blur and geometric distortions in acquired imagery. The ability to distinguish true scene motion from turbulence warping is important for many image processing and analysis tasks. We present two novel scene-motion detection algorithms specifically designed to operate in the presence of anisoplanatic optical turbulence.The first method is based on modeling background intensity fluctuations with a Gaussian mixture model (GMM). The GMM parameters are formed using knowledge of the theoretical turbulence tilt variance statistics derived from the Fried parameter or refractive index structure function. Thus, this new method is referred to as the Tilt Variance GMM (TV-GMM) algorithm. While most prior intensity methods use empirical temporal data to estimate a background model, this approach is based on the theoretical atmospheric tilt variance statistics. This approach effectively avoids contamination in the background statistics when true scene motion is present. The approach also considers the application of global image registration as a preprocessing step to improve performance by employing the recently developed residual tilt variance analysis that accounts for image registration as described in [1].Rather than forming its statistical model on the (optionally registered) input imagery directly, the second method uses a turbulence simulator. Multiple realizations of atmospheric turbulence are applied to a single prototype background image to create a non-contaminated image stack of the scene background. To incorporate the spatial relationship between neighboring pixels, each pixel's intensity is treated as an independent variable of a single Gaussian distribution. Although full anisoplanatic turbulence simulators are available, alternative approaches are sufficient provided that the anisoplanatic warping is accurate. In this work, a warping simulator based on the tilt field statistics described in [2] is used to provide a fast and reasonable approximation. Because this second method uses a warping simulator, it is referred to as the Warping Simulator Gaussian Model (WS-GM) algorithm.Both quantitative and qualitative performance analyses are conducted, and the proposed methods are compared against several state-of-the art algorithms using both synthetic and real world data sets. The three synthetic image datasets are generated with an anisoplanatic numerical wave-propagation simulator [3] that enables per-pixel motion truth. Additionally, TV-GMM and WS-GM are extended to operate on imagery containing multiple color bands, referred to as Multiband TV-GMM (MB-TV-GMM) and Multiband WS-GM (MB-WS-GM), respectively. Both TV-GMM and WS-GM outperform the benchmark methods in the study on grayscale imagery, and MB-TV-GMM and MB-WS-GM outperform their grayscale counterparts.


Electrical Engineering, Atmospheric Sciences, motion detection, turbulence modeling and mitigation, anisoplanatic turbulence simulation, non-contaminated turbulence model, residual tilt variance

Rights Statement

Copyright 2021, author