Albert D. Sheffer

Generation and application of high-resolution infrared computer imagery

The generation of high-fidelity simulated infrared imagery requires a unique combination of physical principles and computer image generation technology. At the Georgia Tech Research Institute infrared simulation software has been developed that couples three-dimensional geometric models with geographic databases, infrared radiance prediction models, and computer graphics techniques for image rendering to generate high-resolution synthetic infrared imagery. These features provide a large degree of flexibility to the simulation and allow it to be employed over a wide spectrum of applications. A discussion of a simulation methodology, a review of the GTVISIT scene simulation tool, and a discussion of several applications are given.

High-fidelity infrared scene simulation at Georgia Tech

The Georgia Tech Research Institute has for more than fifteen years developed and used digital scene models for IR simulation applications. Initially focusing on synthetic scenes of small extent but very high resolution (less than one meter), more recently emphasis has shifted to larger scenes derived from measured data sources with resolution at one meter or slightly greater. One reason for the shift in emphasis has been the emergence of the GTSIMS simulation environment, in which digital IR seeker and missile models and models of other EO/IR sensor systems used in tactical missile engagement scenarios require larger scene extents (typically three to ten kilometers on a side) because of their potential viewing geometries and fields of view. In GTSIMS these sensor and missile models are integrated in a unified software system with the IR scene models and the image rendering software that has been developed along with them. The GTSIMS missile engagement capabilities, including many aspects of scene configuration and signature prediction, are tied together through a graphical user interface called XGTSIMS. This paper will discuss recent IR scene models developed for GTSIMS, from the methodologies used to create the data sets behind the models to the use of these models in GTSIMS via XGTSIMS, then will proceed to discuss current and planned efforts toward real-time image generation of large, complex scenes for IR simulation purposes.

Biologically based vision simulation for target-background discrimination and camouflage/LO design

The Georgia Tech Research Institute has developed an integrated suite of software for Visual and Electro-Optical (VISEO) detection analysis, under the sponsorship of the Army Aviation and Troop Command, Aviation Applied Technology Directorate. The VISEO system is a comprehensive workstation-based tool for multi-spectral signature analysis, LO design, and visualization of targets moving through real measured backgrounds. A key component of the VISEO system is a simulation of real measured backgrounds. A key component of the VISEO system is a simulation of human vision, called the Georgia Tech Vision (GTV) simulation. The algorithms used in the simulation are consistent with neurophysiological evidence concerning the functions of the human visual system, from dynamic light adaptation processes in the retinal receptors and ganglia to the processing of motion, color, and edge information in the striate cortex. The simulation accepts images seen by the naked eye or through direct-view optical systems, as well as images viewed on the displays of IR sensors, image intensifiers and night-vision devices. GTV outputs predicted probabilities that the target is fixated (Pfix) during visual search, and detected (Pd), and also identifies specific features of the target that contribute most to successful search and detection performance. This paper outlines the capabilities and structure of the VISEO system, emphasizing GTV. Example results of visible and IR signature reduction on the basis of VISEO will be shown and described.

Ocean background model for scene simulation

An ocean surface model for synthetic IR/visible imaging applications has been developed at GTRI based upon the Pierson-Moskowitz wave spectrum. The model calculates a 2D grid of height values describing a given snapshot of the sea surface. This surface is a function of wind speed and direction as well as elapsed time into the simulation. The time parameter permits the animation of the sea surface during a simulated movie sequence. Sea signatures are calculated using a combination of models and tools: the GTRI IR signature code GTSIG is used to predict sea temperatures; LOWTRAN7 is used to construct tables of sky radiances; the Fresnel equations are used to construct tables of sea reflectance. These signature components are combined during image rendering with a ray-tracing approach the provides the total radiance (emitted and reflected) from the ocean surface arriving at each sensor image pixel. This ocean model has been integrated into a complete image rendering system called GTRENDER, and is available for use in GTRI applications such as the GTSIMS family of missile simulations.

Simulation Of Laser Radar Imagery

Software has been developed for the simulation of laser radar range imagery. Two versions have been developed: the first is an idealized model which is noise-free and with zero dropout rate; the second includes both pointing and range noise effects and provides for calculation of probability of detection for each pixel, with dropout occurring for probabilities below threshold, and also allows for user control over a number of other parameters such as scanning convention (unidirectional vs. bidirectional), scan efficiency, and trajectory update rates. Each version allows for motion of a LADAR sensor across a terrain database on which faceted objects (targets and clutter) have been placed. For each pixel the program calculates the laser exit beam direction, based upon the combined effects of the sensor sweep pattern and the motion and attitude of the sensor platform. The exit beam is traced for intersection with the terrain or an object. Program output consists of the x,y,z-coordinates of the intersection point and the (real-number) range to that point for each pixel. This output can then be converted to a displayable range image. The software is currently implemented on a VAX 11/750 computer operating under VMS.

Incorporation of measured natural reflectivities in a background clutter simulation

Modeling of material reflectance in simulated infrared/electro-optical scenes is typically done assuming a Gaussian distribution or similar simple statistical model, because of limited available measured data. Such an approach fails to capture the true reflectance statistics for active systems such as laser sensors. A new approach developed for a data-rich environment will be described, as applied to a laser sensor simulation. This approach utilizes a large database of recently collected laser sensor data to derive reflectance histograms for each material type in a scene. Simulated imagery using such sampled histograms is much more faithful to actual system imagery than that based on traditional statistical models. The paper will describe the material database and the algorithms by which it is utilized in the simulation, and will present resulting simulated imagery and comparisons to simulated imagery using Gaussian reflectivity models.

Background clutter models for scene simulation

Geographic background models constitute an important component in scene simulations as they provide one component of clutter observed through IR sensors. Accurate modeling of the spatially-varying features and signatures of the background presents a significant challenge to the developer of a scene simulation; especially for tactical applications. The approach employed within the Georgia Tech Research Institute is outlined in this paper. Emphasis is placed on the methodology for creating the spatial structure of the background, the underlying signature prediction model, and the relationship between the geographic features and the signature model.

Computer-based system for target identification training based on synthetic image generation of infrared scenes

Development of a computer-based system for generating simulated infrared imagery is described. The system provides realistic representations of infrared targets and backgrounds for training soldiers in combat vehicle identification (CVI). Using Army-supplied lists of desired items, Georgia Tech Research Institute (GTRI) constructed geometric and thermal models of NATO and Warsaw Pact combat vehicles, terrain backgrounds, countermeasures, distractors, and obscurants. Simulations of several fielded infrared sensors enable system users to generate training imagery sets, both snapshots and animated sequences, showing realistic sensor effects. The system is workstation-based and has a user interface that permits a non-expert to generate desired imagery sets from menus of available models and scenarios.

Computer-Generated Infrared Imagery for Combat-Vehicle Identification Training

Development of a computer-based system for generating simulated infrared (IR) imagery is described. The system provides realistic representations of IR targets and backgrounds for training soldiers in combat vehicle identification (CVI). Simulations of several fielded IR sensors enables system users to generate training imagery sets, both snapshots and animated sequences, showing realistic sensor effects. The system is workstation-based and has a user interface that permits a non-expert to generate desired imagery sets from menus of available models and scenarios.

Synthetic visible imagery for multiattribute target identification

A visible band signature model is under development at the Georgia Tech Research Institute (GTRI) to support research activities ranging from performance studies of human observers to the definition and development of feature extraction algorithms. This model generates visible band imagery based on a solar illumination model coupled with computer graphics rendering algorithms. The solar illunination model employs a modified version of a radiative transfer algorithm originally developed for the Air Force. Selection of an appropriate reflection model followed an evaluation of several techniques. The selection criteria and results of the survey are presented.