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The emergence of machine learning into scientific fields has created opportunities for novel and powerful image processing techniques. Algorithms that can perform complex tasks without a “man-in-the-loop” or explicit instructions are invaluable artificial intelligence tools. These algorithms typically require a large set of training data on which to base statistical predictions. In the case of electro-optical infrared (EO/IR) remote sensing, algorithm designers often seek a substantial library of images comprising many weather conditions, times of day, sensor resolutions, etc. These images may be synthetic (predicted) or measured, but should encompass a large variety of targets imaged from a variety of vantage points against numerous backgrounds. Acquiring such a large set of measured imagery with sufficient variation can be difficult, requiring numerous field campaigns. Alternatively, accurate prediction of target signatures in cluttered outdoor scenes may be a viable option. In this work, sensor imagery is generated using CoTherm, a co-simulation tool which operates MuSES (an EO/IR simulation code) in an automated fashion to create a large library of synthetic images. The relevant MuSES inputs – which might include environmental factors, global location, date and time, vehicle engine state, human clothing and activity level, or sensor waveband – can be manipulated by a CoTherm workflow process. The output of this process is a large library of MuSES-generated EO/IR sensor radiance images suitable for algorithm development. If desired, synthetic target pixels can be inserted into measured background images for added realism.

The ability to accurately predict electro-optical signatures for high-value targets in an outdoor scene is a tremendous asset for defense agencies. In the thermal infrared wavebands, physical temperature is the primary contributor to imager-detected radiance. Consequently, enhancements to the fidelity of thermal predictions are desirable, and convection estimates are often the most significant source of uncertainty. One traditional method employed by MuSES applies a single global convection coefficient across the entire scene, and assumes that all exposed surfaces are in contact with the ambient air temperature. This results in a rapid prediction of convection coefficients for a large scene that changes dynamically with wind speed but lacks localized detail concerning how each target surface can experience a different convection coefficient and local air temperature. In practice, wind speed and directions typically change frequently, which coupled with the thermal mass of most targets reduces the negative impacts this approach can have on thermal predictions; numerous validations of MuSES predictions bear this out. Computational fluid dynamics (CFD) simulations provide additional spatial fidelity in the calculation of localized convection coefficients and air temperatures but only at great computational cost. A fully transient outdoor scene simulation would be nearly impossible. Boundary layers near surfaces must be resolved with a fine mesh, creating a numerical problem difficult to solve for large spatial scales when spanning large periods of simulated time. In this paper, a novel thermal fluid flow solver is presented. This proprietary flow solver models fluid flow at the spatial resolution and accuracy needed for convective heat transfer at the scale viewed by EO/IR (Electro-optical/Infrared) sensors, avoiding the burdens associated with conventional CFD codes. Many of the same Navier-Stokes equations are solved, albeit in simpler form. ThermoAnalytics-developed correlations directly calculate convection coefficients based on local bulk flow conditions of temperature, velocity, and pressure. The resulting accuracy is a large step beyond using a constant convection correlation universally across the scene. Intelligent simplification of the flow equations provides robust efficiency, requiring minimal effort and expertise compared to CFD codes.

The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been extensively validated and provides a flexible process for signature evaluation and algorithm development.

The ability to predict electro-optical (EO) signatures of diverse targets against cluttered backgrounds is paramount for signature evaluation and/or management. Knowledge of target and background signatures is essential for a variety of defense-related applications. While there is no substitute for measured target and background signatures to determine contrast and detection probability, the capability to simulate any mission scenario with desired environmental conditions is a tremendous asset for defense agencies. In this paper, a systematic process for the thermal and visible-through-infrared simulation of camouflaged human dismounts in cluttered outdoor environments is presented. This process, utilizing the thermal and EO/IR radiance simulation tool TAIThermIR (and MuSES), provides a repeatable and accurate approach for analyzing contrast, signature and detectability of humans in multiple wavebands. The engineering workflow required to combine natural weather boundary conditions and the human thermoregulatory module developed by ThermoAnalytics is summarized. The procedure includes human geometry creation, human segmental physiology description and transient physical temperature prediction using environmental boundary conditions and active thermoregulation. Radiance renderings, which use Sandford-Robertson BRDF optical surface property descriptions and are coupled with MODTRAN for the calculation of atmospheric effects, are demonstrated. Sensor effects such as optical blurring and photon noise can be optionally included, increasing the accuracy of detection probability outputs that accompany each rendering. This virtual evaluation procedure has been extensively validated and provides a flexible evaluation process that minimizes the difficulties inherent in human-subject field testing. Defense applications such as detection probability assessment, camouflage pattern evaluation, conspicuity tests and automatic target recognition are discussed.

In principle, infrared (IR) imagery of exposed skin and clothing can provide a valuable source of data for thermo-physiological studies. In practice, and despite the fall in the cost of IR cameras, infrared imagery is not universally collected during human subject testing. One reason for this may be the relatively poor accuracy of IR cameras (typically ±2 °C). The repeatability of the measurements taken with a particular IR camera (both spatially and temporally) is much better than its accuracy, i.e., close to the camera's sensitivity. An IR camera's sensitivity (and repeatability) is typically on the order of hundredths of a degree Celsius. Consequently, a reference with known temperature and emissivity placed in the camera's field-of-view when taking measurements can provide two potential benefits: First, measurement error can be reduced by using a calibration procedure. Second, IR imagery taken by different IR cameras during different test episodes can be directly compared.

The linear stability of a planar and a barred coast is studied with a complex processbased model. By applying either the Engelund and Hansen or the Bailard sediment transport formulation, and a wave angle of approximately 5° in the breaker zone, the sensitivity of the linear stability characteristics to the sediment transport formulation is explored. Applying Engelund and Hansen to planar beaches, no fastest growing mode is found since the growth rate continuously increases with increasing wavelength. The corresponding bed perturbations are very oblique, down-current oriented bars. With Bailard, on the contrary, the growth rate increases with decreasing wavelength, suggesting that the preferred wavelength is somewhere between 0 and 300 m. The corresponding bed forms are up-current bars. Furthermore, the results show that inclusion of sediment transport in the direction of the wave orbital motion in both formulations is crucial for the growth of bed perturbations. When only (bed load) transport in the direction of the mean current is applied, stable up-current oriented bars are found. On barred beaches, both the Engelund and Hansen, and the Bailard formulation result in growing bed perturbations, consisting of rip channels around the crest of the breaker bar. Furthermore, in both cases fastest growing modes are found. The Bailard formulation results in a smaller longshore spacing of the rip channel system than the Engelund and Hansen formulation.

The linear stability of singleand double-barred coasts is studied. For a single-barred beach, the wavelength of the fastest growing mode increases with increasing trough width. The maximum growth rate itself decreases with increasing distance between the shore and the crest of the bar. The spatial structure is an undulating pattern and is independent of the alongshore wavelength of the perturbation. These results show good qualitative agreement with a previous study. The stability of double-barred beaches is studied, fixing the alongshore wavelength, with the focus on the sensitivity of the growth rate and the spatial pattern to two geometrical parameters, viz. the crest height of the outer breaker bar and the distance between the crests of the two breaker bars. The spatial structure at the position of the inner bar is similar to the undulating pattern found in the single-barred experiments. The amplitude of the perturbation on the outer breaker bar depends on the distance between the breaker bars and on the height of the outer breaker bar.

This paper presents the numerical hindcasting of the first eleven days of the main experiment of the Coast3D project, carried out near Egmond (the Netherlands) in the autumn of 1998. In this period a rip channel develops with an orientation correlated with the wave direction. Hindcasting the wave height and velocity measurements with a numerical model resulted in an increasing underestimation of wave breaking going closer to the shore. Consequently, there is good agreement between the computed and measured longshore velocities on the seaward slope of the inner breaker bar (Station lA), but less so for these velocities on the shoreward slope of the inner breaker bar (Station 1D). We expect this to be due to the inability of the wave model to predict rapid decrease of the wave height across the inner bar. Crossshore velocities in the main transect are dominated by rip currents and wave breaking-induced return flow. Qualitative agreement is fairly good but quantitative agreement between the measurements and the model results is only reasonable, probably caused by the application of a 2DH mode, inaccurate prediction of the location of rip currents and differences in the bathymetry. INTRODUCTION The COAST3D measurement campaigns near the town of Egmond, the Netherlands, resulted in two extensive data sets. Data of the first (pilot) field campaign was used to 1) Researcher, Section of Hydraulic Engineering, Delft University of Technology, P.O.Box 5048, 2600 GA Delft, The Netherlands. m.d.klein@ct.tudelft.nl 2) Researcher, Section of Hydraulic Engineering, Delft University of Technology, P.O.Box 5048, 2600 GA Delft, The Netherlands. e.p.l.elias@ct.tudelft.nl 3) Professor, WLJDelft Hydraulics, P.O. Box 177, 2600 MH Delft, The Netherlands. marcel.stive@wldelft.nl 4) Senior Researcher, WLlDelft Hydraulics, P.O. Box 177, 2600 MH Delft, The Netherlands. dirkj an.walstra@wldel ft.nl