J. Michael Cathcart

Design considerations for parasitic use of synchrotron radiation in the infrared

Abstract Computer calculations are used to generate a feasibility study for the parasitic utilization of infrared radiation from a synchrotron radiation source. The calculations demonstrate that intercepting all but 4 to 6 mrad of the central cone will result in the hard radiation passing through an aperture with negligible loss in the intensity of the hard radiation and at the same time the long wavelength radiation will be effectively intercepted.

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.

Adaptive Spatial Sampling Schemes for the Detection of Minefields in Hyperspectral Imagery

Often in hyperspectral overhead land mine imagery, there exists clutter with similar spatial and spectral characteristics to those of land mines. However groups of clutter features are rarely related spatially in the same way that groups of mines are related. For this reason, recognition of field patterns in overhead land mine imagery is critical to the detection of mine fields. The material presented here addresses means by which to spatially sample overhead hyperspectral imagery for the accentuation of mine field patterns. Our initial approach is to assume that the mines are laid out in a particular field pattern. We then search for spectral anomalies that are spatially distributed according to such a pattern. For this purpose, we utilize an RX detector with locally estimated mean and covariance matrix. We then use the pattern to predict the locations of additional mines. These locations provide us with search regions for the use of a second anomaly detector, in this case we use an anomaly detector based upon an eigenspace separation transform. Examples are provided using LWIR imagery.

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.

Spectral analysis of terrain infrared signatures

Research conducted at Georgia Tech over the past several years has focused on an examination of the signature characteristics of various background materials. These efforts seek to understand the physical basis and features of these signatures in order to aid the development of robust landmine detection techniques. The technical efforts in this study focused on identifying various soil types in LWIR hyperspectral imagery based on spectral characteristics. This paper will discuss the analytical approach and present results from these studies. A discussion of these terrain features as false alarm and clutter sources will also be presented.

Phenomenological investigations into personnel signatures

Georgia Tech has initiated a research program into the issues surrounding the detection of covert personnel present in a wide variety of scenarios. These initial investigations have been focused on a detailed phenomenological analysis of human physiology with the subsequent identification and characterization of potential observables-particularly in the context of urban environments during this phase. A parallel effort focused on the characterization of the resulting humanspecific signatures and their dependency and variation on local environmental conditions. In addition, these studies established the basic requirements for the development of physics-based human signature models; a significant component of these models is the inclusion of environmental and physiological variables into the computations. This paper will present a review of the research program and preliminary results from several of the initial signature phenomenology tasks.

OREOS: a new EO-IR modeling and simulation tool for U.S. Coast Guard search and rescue applications

Georgia Tech has developed a new modeling and simulation tool that predicts both radar and electro-optical infrared (EO-IR) lateral range curves (LRCs) and sweep widths (SWs) under the Optimization of Radar and Electro-Optical Sensors (OREOS) program for US Coast Guard Search and Rescue (SAR) applications. In a search scenario when the location of the lost or overdue craft is unknown, the Coast Guard will conduct searches based upon standard procedure, personnel expertise, operational experience, and models. One metric for search planning is the sweep width, or integrated area under a LRC. Because a searching craft is equipped with radar and EO-IR sensor suites, the Coast Guard is interested in accurate predictions of sweep width for the particular search scenario. Here, we will discuss the physical models that make up the EO-IR portion of the OREOS code. First, Georgia Tech SIGnature (GTSIG) generates thermal signatures of search targets based upon the thermal and optical properties of the target and the environment; a renderer then calculates target contrast. Sensor information, atmospheric transmission, and the calculated target contrasts are input into NVESD models to generate probability of detection (PD) vs. slant range data. These PD vs. range values are then converted into LRCs by taking into account a continuous look search from a moving platform; sweep widths are then calculated. The OREOS tool differs from previous methods in that physical models are used to predict the LRCs and sweep widths at every step in the process, whereas heuristic methods were previously employed to generate final predictions.

Automated determination of scale and orientation of mine field grid

Mine fields are often distinguishable in overhead hyperspectral LWIR imagery due to the spatial pattern in which the mines are laid. Recognition of these field patterns in overhead landmine imagery shows promise for enhancing the ability to detect mine fields. However, before one can search for a field pattern in an image, it is necessary to determine the orientation and size of the pattern within the image, should it exist. We present a method for determining likely scales and orientation for grids of landmines. The approach is to consider pairs of interest points and then look for patterns in the slopes of the lines connecting them. The dominant slope then determines an orientation angle. Next, we look for patterns in the distances between pairs of points that have a slope close to the orientation angle. An application to detecting mine fields via recognition of patterns of features in hyperspectral LWIR imagery is given.

Electro-optical signature analysis for personnel detection in urban environments

Georgia Tech has initiated a research program into the detection of covert personnel. This program focuses on the detection problem in scenarios focused on urban operations, tunnels, or convoys. These nontraditional operational scenarios present multiple opportunities for personnel to hide as well as a variety to clutter levels. The research program focuses on a detailed phenomenological analysis of human physiology and signatures with the subsequent identification and characterization of potential observables - initially in the context of urban environments. For this current effort, several electro-optical sensing modalities have been evaluated for use as a component in an unattended sensor suite designed to detect personnel. These modalities include active sensors (e.g., vibrometry) and passive sensors (e.g., multispectral, thermal). Particular emphasis has been given to the investigation of short wave infrared signatures and the comparison of this band to the other electro-optical wavebands. This paper will discuss the design of a multi-spectral signature model which forms a component of the evaluation process. Example results will be presented as well as a discussion of the issues to be addressed as part of the electro-optical sensor evaluation.

Statistical analysis of spectral data for vegetation detection

Identification and reduction of false alarms provide a critical component in the detection of landmines. Research at Georgia Tech over the past several years has focused on this problem through an examination of the signature characteristics of various background materials. These efforts seek to understand the physical basis and features of these signatures as an aid to the development of false target identification techniques. The investigation presented in this paper deal concentrated on the detection of foliage in long wave infrared imagery. Data collected by a hyperspectral long-wave infrared sensor provided the background signatures used in this study. These studies focused on an analysis of the statistical characteristics of both the intensity signature and derived emissivity data. Results from these studies indicate foliage signatures possess unique characteristics that can be exploited to enable detection of vegetation in LWIR images. This paper will present review of the approach and results of the statistical analysis.