KEYWORDS: Sensors, Electro optical modeling, Atmospheric modeling, RGB color model, Data modeling, 3D modeling, Vegetation, Mid-IR, General packet radio service, Systems modeling
The U.S. Army Engineer Research and Development Center (ERDC) developed a near-surface computational testbed
(CTB) for modeling geo-environments. This modeling capability is used to predict and improve the performance of
current and future-force sensor systems for surface and near-surface threat detection for a wide range of geoenvironments.
The CTB is a suite of integrated models and tools used to approximately replicate geo-physical processes
such as radiometry, meteorology, moisture transport, and thermal transport that influence the resultant signatures of both
natural and man-made materials, as perceived by the sensors. The CTB is designed within a High Performance
Computing (HPC) framework to accommodate the size and complexity of the virtual environments required for
analyzing and quantifying sensor performance. Specifically, as a rule-of-thumb, the size of the scene should encompass
an area that is at a minimum, the size of the spatial coverage of the sensor. This HPC capability allows the CTB to
replicate geophysical processes and subsurface heterogeneity with high levels of realism and to provide new insight into
identifying the geophysical processes and environmental factors that significantly affect the signatures sensed by
multispectral imaging, near-infrared, mid-wave infrared, long-wave infrared, and ground penetrating radar sensors.
Additionally, this effort is helping to quantify the performance and optimal time-of-use for sensors to detect threats
within highly heterogeneous geo-environments by reducing false alarms from automated target recognition algorithms.
The U.S. Army Engineer Research and Development Center (ERDC) has developed a suite of models that
replicate the signicant geo-physical processes which aect the thermal signatures sensed by infrared imaging
systems. This suite of models also includes an electro-optical/infrared (EO/IR) sensor model that produces
synthetic thermal imagery. The EO/IR sensor model can be adapted to replicate the performance of other
infrared sensor systems as well.
It is well known that eld-collected IR imagery can be in
uenced by the micro-topographic features of a
particular location. As a result, the performance of automated target recognition algorithms and decisions based
on their results can also be aected. Other signicant contributors to false alarms and issues with probabilities-of-
detection include the relative locations of vegetation and local changes in soil types or properties. For example, a
change in the retention of soil moisture alone is known to contribute to false alarms due to changes in radiative and
thermal properties of wet versus dry soil. Many aspects of eld data collection eorts (weather, soil uniformity,
etc.) cannot be controlled nor changed after the fact. Within a computational framework, however, plant and
object locations, as well as weather patterns can, all be changed. In this work, the sensitivity of simulated IR
imagery will be examined as it relates to initial states and boundary forcing terms due to weather conditions.
Dierent approaches to these inputs will be examined using the computational testbed developed at the ERDC.
This paper analyzes the UXO classification capabilities of the GEM-3 using data collected for the Advanced UXO Detection/Discrimination Technology Demonstration at the U.S. Army Jefferson Proving Ground (JPG), Madison, Indiana. The approach taken in the US Army Engineer Research and Development Center (ERDC) analysis of the performance of the GEM-3 at JPG was to extract data points collected near each of the actual target locations and compare them to the calibration data acquired with known targets at the beginning of the demonstration. This was done to determine how well the data collected near each actual target matched the calibration signatures for the same ordnance type and the extent to which the data could be differentiated from other ordnance types and non-ordnance clutter. Classification of the targets was performed using a simple template-matching algorithm. This procedure resulted in an exact classification match for nearly half of the targets for which calibration data were available and a match to a similarly sized target for more than two-thirds of the medium and large targets. The sensor coverage of the test areas and the effect of test parameters such as ordnance size and depth on classification performance were also examined. New data were acquired with the GEM-3 to investigate the statistical variability of the instrument.
A complete Stokes imaging spectropolarimeter has been designed, constructed and tested by researchers at the U.S. Army Engineer Research and Development Center (USAERDC) in collaboration with researchers at the University of Arizona's Optical Sciences Center. CTISP is a polarimetric extension to CTIS, developed by the authors associated with the Optical Sciences Center. Currently, CTISP characterizes an object's spectropolarimetric radiance over the 440 to 740 nm range using 20 nm spectral bins and subdividing the FOV with a 32 X 32 resolution. CTISP's output is a four-fold increase in object cube information when compared to spectral radiance alone as CTISP effectively extracts the polarization information from the radiance of each of the 'N' voxels to form 'N' 4 element Stokes vectors, where N equals (# horizontal resolution elements in FOV)*(# vertical resolution elements in FOV)*(# of wavelength bands). Voxel polarization calibration is performed using a fully computer automated spectropolarimetric calibration facility. The facility generates an object whose spatial and spectral dimensions define a voxel and whose radiance is purely polarized. CTISP's response to each generated polarized voxel is recorded and used to calculate a polarization characteristic matrix (PCM) for each voxel. CTISP utilizes four polarization analyzers in an automated rotating filter wheel configuration to acquire four images of the object. The results from the image reconstructions behind four analyzers are utilized with the PCMs to estimate the Stokes vector for each voxel in the object cube. CTISP utilizes a host of software tools to control the calibration facility, perform image acquisition and perform reconstruction and Stokes vector calculation. The order of use and inter-relation of these tools is described. Results will be presented and indicate that CTISP is capable of reconstructing objects containing complex spectral, spatial and polarization content. A spectral comparison is made to a reference spectrometer using a reflectance standard for normalization.
Researchers at the US Army Engineer Research and Development Center, in collaboration with researchers at the University of Arizona's Optical Sciences Center, have designed, constructed and developed a complete Stokes imaging spectropolarimeter. CTISP is a polarimetric extension to CTIS, developed by the authors associated with the Optical Science Center. Currently, CTISP characterizes an object's spectropolarimetric radiance over the 440 to 740 nm range using 20 nm spectral bins and subdividing the FOV with a 32 by 32 resolution for a total of 16 by 32 by 32 equals voxels. The output of CTISP is an estimate of the Stokes vector for each voxel.
A remote minefield detection system (REMIDS) developed as part of the U.S. Army's Standoff Minefield System Research Program is presented. This helicopter-mounted system based on an active/passive line scanner, real-time processing, and display and navigational equipment obtains image data in three principal coregistered channels via line scanning. Two channels provide near-IR linear polarization reflectance vector information while the third channel provides passive thermal information. Numerous flight tests showed that the REMIDS system is capable of detecting mines during both day and night flight. Polarization is confirmed to be are a good discriminator between man-made and natural objects. Active polarization and reflectance information proved to be superior to thermal data in several natural scenarios including arid regions, overcast conditions, and diurnal thermal crossover periods.
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