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.
Earth’s atmosphere can significantly impact the propagation of electromagnetic radiation, degrading the performance of imaging systems. Deleterious effects of the atmosphere include turbulence, absorption and scattering by particulates. Turbulence leads to blurring, while absorption attenuates the energy that reaches imaging sensors. The optical properties of aerosols and clouds also impact radiation propagation via scattering, resulting in decorrelation from unscattered light. Models have been proposed for calculating a point spread function (PSF) for aerosol scattering, providing a method for simulating the contrast and spatial detail expected when imaging through atmospheres with significant aerosol optical depth. However, these synthetic images and their predicating theory would benefit from comparison with measurements in a controlled environment. Recently, Michigan Technological University (MTU) has designed a novel laboratory cloud chamber. This multiphase, turbulent “Pi Chamber” is capable of pressures down to 100 hPa and temperatures from -55 to +55°C. Additionally, humidity and aerosol concentrations are controllable. These boundary conditions can be combined to form and sustain clouds in an instrumented laboratory setting for measuring the impact of clouds on radiation propagation. This paper describes an experiment to generate mixing and expansion clouds in supersaturated conditions with salt aerosols, and an example of measured imagery viewed through the generated cloud is shown. Aerosol and cloud droplet distributions measured during the experiment are used to predict scattering PSF and MTF curves, and a methodology for validating existing theory is detailed. Measured atmospheric inputs will be used to simulate aerosol-induced image degradation for comparison with measured imagery taken through actual cloud conditions. The aerosol MTF will be experimentally calculated and compared to theoretical expressions. The key result of this study is the proposal of a closure experiment for verification of theoretical aerosol effects using actual clouds in a controlled laboratory setting.
Accurate infrared signature prediction of targets, such as humans or ground vehicles, depends primarily on the realistic prediction of physical temperatures. Thermal model development typically requires a geometric description of the target (i.e., a 3D surface mesh) along with material properties for characterizing the thermal response to simulated weather conditions. Once an accurate thermal solution has been obtained, signature predictions for an EO/IR spectral waveband can be generated. The image rendering algorithm should consider the radiative emissions, diffuse/specular reflections, and atmospheric effects to depict how an object in a natural scene would be perceived by an EO/IR sensor. The EO/IR rendering process within MuSES, developed by ThermoAnalytics, can be used to create a synthetic radiance image that predicts the energy detected by a specific sensor just prior to passing through its optics. For additional realism, blurring due to lens diffraction and noise due to variations in photon detection can also be included, via specification of sensor characteristics. Additionally, probability of detection can be obtained via the Targeting Task Performance (TTP) metric, making it possible to predict a target’s at-range detectability to a particular threat sensor. In this paper, we will investigate the at-range contrast and detectability of some example targets and examine the effect of various techniques such as sub-pixel sampling and target pixel thresholding.