This PDF file contains the front matter associated with SPIE Proceedings Volume 7687, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

We introduce an approach to the efficient recognition of families of surface shapes in range images. This builds upon earlier work on Tripod Operators (TOs), a method for extracting small sets of N points from 3D surface data in a canonical way such that coordinate independent shape descriptions can be efficiently generated and compared. Using TOs, a specific surface shape generates a signature which is a manifold of dimension ≤ 3 in a feature space of dimension d = N - 3. A runtime application of a TO on surface data generates a d-vector whose distance from the signature manifold is closely related to the likelihood of a match. Ordnance identification is a motivating application. In order to use TOs for recognizing objects from large sets of known shapes, and families of shapes, we introduce the use of manifold learning to represent the signature manifolds with piecewise analytic descriptions instead of discrete point sets. We consider the example of generalizing the signatures of several artillery shells which are qualitatively the same in shape, but metrically different. This can yield a signature that is only slightly more complex than the originals, but enables efficient recognition of a continuous family of shapes.

Anomaly detection methods applied to LAser Detection And Ranging (LADAR) data have traditionally utilized intensity and range as indicators. The proposed method for anomaly detection presented in this paper uses information about the range profile of the target to determine if the target is anomalous. The range profile is related to the range depth of the target surface relative to the illuminating wave of the LADAR system. If the target surface is slanted for instance, the range to the target will vary as a function of position within the instantaneous field of view of the LADAR receiver optics. This variation of range can be indicative of the presence of an anomaly within the scene viewed by the system. The ability to detect range variation from a returned LADAR pulse is a feature of the Normalized VAriable Shape (NOVAS) correlator. The algorithm computes both the range to the target and the shape of the pulse adaptively using an assumed mathematical form for the shape of the pulse. In the cases examined in this paper a Gaussian pulse shape is assumed for the pulse returning from the target. The NOVAS algorithm estimates the standard deviation of the Gaussian as well as the range associated with the shift in its position in time. The NOVAS algorithm achieves its ability to discern both the range to the target and the shape of the returning pulse through the use of the Pearson's product coefficient. This normalized correlation operation searches over both range and pulse shape parameter to achieve the highest correlation value between the pulse model and the reflected pulse measurement. The range depth of the target can be inferred from the returning pulse width as the pulse shape reflected from the target is the result of a convolution between the pulse fired at the target and the range profile.

Estimates of surface terrain electromagnetic properties can be utilized by Computational Electromagnetic Modeling (CEM) software to predict radio signal propagation loss between a transmitter and receiver. This paper will examine the variability of the dielectric properties of surface soils as a function of composition, moisture content, and frequency using a semi-empirical model from literature. Using the CEM software the signal path loss will be calculated and the effects of the variability in the dielectric constant of soil examined. High resolution remote sensing imagery will be considered as a data source for soil composition and moisture content information. This topic has implications on using modeling and simulation to understand and predict the performance of RF ground sensors and systems.

We are developing tunable-multi-wavelength resonance-Raman spectroscopy and algorithms to enable rapid detection and identification of bacteria and chemicals in complex environments. The system, dubbed SWOrRD, is capable of illuminating a sample containing many chemicals or biological agents with a sequence of laser wavelengths between 210nm and 2000nm; a range which encompasses the resonant frequencies of cells, microorganisms, cellular metabolites, and many chemicals; and measures the resonance-Raman spectra of light scattered from the sample at each laser wavelength. These multiple spectra, which contain much more information about the bond structure that is contained in a single spectrum, are analyzed by a linear-mixture algorithm, based on NRL's ORASIS, to determine the chemical and bacteriological constituents of the sample. The current status of the research will be described.

The method, which gives us a possibility to obtain the unique 2D signature of substance, for its identification in THz frequency range is developed and applied for the treatment of signals, passed through ordinary materials or selected explosives, including those hidden under opaque simulant covers. The method of identification is based on the analysis of spectrum dynamics (spectrogram) of medium response and has the ability not only to detect the presence of the substance in the sample but to identify it by its 2D signature, which is unique for each investigated substance. It allows to trace the dynamics of many spectral lines in one set of measurements simultaneously and to obtain the full information about the spectrum dynamics of the measured signal. We showed that spectrograms of THz pulses, passed through the explosives, hidden under simulant covers, widely differ from spectrograms of simulant themselves despite of a little difference in their Fourier spectra. Therefore, the method allows detecting and identifying the hidden substances with high probability and can be very effective for defense and security applications. The problem of detection of a noisy regular acoustic signal with linear modulation of frequency is examined too.

Many materials such as drugs and explosives have characteristic spectral signatures in the terahertz (THz) band. These unique signatures hold great promise for potential detection utilizing THz radiation. While such spectral features are most easily observed in transmission,real life imaging systems will need to identify materials of interest from reflection measurements,often in non-ideal geometries. In this work we investigate the interference effects introduced by layered materials,whic h are commonly encountered in realistic sensing geometries. A model for reflection from a layer of material is presented,along with reflection measurements of single layers of sample material. Reflection measurements were made to compare the response of two materials; α-lactose monohydrate which has sharp absorption features,and polyethylene which does not. Finally,the model is inverted numerically to extract material parameters from the measured data as well as simulated reflection responses from the explosive C4.

This paper discusses methods developed for measuring the reflectance and transmittance of solid materials in the laboratory using instruments designed for the field. Having the ability to use field instruments to obtain lab-quality measurements negates the need for redundant instrumentation. In our work we use an ABB MR170 Fourier Transform Infrared (FTIR) spectroradiometer to collect infrared spectra of natural and manmade surfaces in a variety of terrains and environments. Our laboratory protocols are optimized for the 3-14μm region of the electromagnetic spectrum. We describe our measurement protocols and present sample data.

Rapid detection of biological material is critical for determining presence/absence of bacterial endospores within various investigative programs. Even more critical is that if select material tests positive for bacillus endospores then tests should provide data at the species level. Optical detection of microbial endospore formers such as Bacillus sp. can be heavy, cumbersome, and may only identify at the genus level. Data provided from this study will aid in characterization needed by future detection systems for further rapid breakdown analysis to gain insight into a more positive signature collection of Bacillus sp. Literature has shown that fluorescence spectroscopy of endospores could be statistically separated from other vegetative genera, but could not be separated among one another. Results of this study showed endospore species separation is possible using laser-induce fluorescence with lifetime decay analysis for Bacillus endospores. Lifetime decays of B. subtilis, B. megaterium, B. coagulans, and B. anthracis Sterne strain were investigated. Using the Multi-Exponential fit method data showed three distinct lifetimes for each species within the following ranges, 0.2-1.3 ns; 2.5-7.0 ns; 7.5-15.0 ns, when laser induced at 307 nm. The four endospore species were individually separated using principle component analysis (95% CI).

Georgia Tech been investigating method for the detection of covert personnel in traditionally difficult environments (e.g., urban, caves). This program focuses on a detailed phenomenological analysis of human physiology and signatures with the subsequent identification and characterization of potential observables. Both aspects are needed to support the development of personnel detection and tracking algorithms. The difficult nature of these personnel-related problems dictates a multimodal sensing approach. Human signature data of sufficient and accurate quality and quantity do not exist, thus the development of an accurate signature model for a human is needed. This model should also simulate various human activities to allow motion-based observables to be exploited. This paper will describe a multimodal signature modeling approach that incorporates human physiological aspects, thermoregulation, and dynamics into the signature calculation. This approach permits both passive and active signatures to be modeled. The focus of the current effort involved the computation of signatures in urban environments. This paper will discuss the development of a human motion model for use in simulating both electro-optical signatures and radar-based signatures. Video sequences of humans in a simulated urban environment will also be presented; results using these sequences for personnel tracking will be presented.

The midwave and longwave infrared regions of the electromagnetic spectrum contain rich information which can be captured by hyperspectral sensors thus enabling enhanced detection of targets of interest. A continuous hyperspectral imaging measurement capability operated 24/7 over varying seasons and weather conditions permits the evaluation of hyperspectral imaging for detection of different types of targets in real world environments. Such a measurement site was built at Picatinny Arsenal under the Spectral and Polarimetric Imagery Collection Experiment (SPICE), where two Hyper-Cam hyperspectral imagers are installed at the Precision Armament Laboratory (PAL) and are operated autonomously since Fall of 2009. The Hyper-Cam are currently collecting a complete hyperspectral database that contains the MWIR and LWIR hyperspectral measurements of several targets under day, night, sunny, cloudy, foggy, rainy and snowy conditions. The Telops Hyper-Cam sensor is an imaging spectrometer that enables the spatial and spectral analysis capabilities using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The MWIR version covers the 3 to 5 μm spectral range and the LWIR version covers the 8 to 12 μm spectral range. This paper describes the automated operation of the two Hyper-Cam sensors being used in the SPICE data collection. The Reveal Automation Control Software (RACS) developed collaboratively between Telops, ARDEC, and ARL enables flexible operating parameters and autonomous calibration. Under the RACS software, the Hyper-Cam sensors can autonomously calibrate itself using their internal blackbody targets, and the calibration events are initiated by user defined time intervals and on internal beamsplitter temperature monitoring. The RACS software is the first software developed for COTS hyperspectal sensors that allows for full autonomous data collection capability for the user. The accuracy of the automatic calibration was characterized and is presented in this paper.

The U.S. Army Research Laboratory (ARL) conducted an initial study on the performance of XML and HDF5 in three popular computational software environments, MATLAB, Octave, and Python, all of which use high-level scripting languages and computational software tools designed for computational processing. Although usable for sharing and exchanging data, the initial results of the study indicated XML has clear limitations in a computational environment. Popular computational tools are unable to handle very large XML formatted files, thus limiting processing of large XML archived data files. We show the breakdown points of XML formatted files for various popular computational tools and explore the performance dependencies of XML and HDF5 formatted files in popular computational environments on the hardware, operating system, and mathematical function. This study also explores the inverse file size relationship between HDF5 and XML data files. Several organizations, including ARL, use both XML and HDF5 for archiving and exchanging data. XML is best suited for storing "light" data (such as metadata) and HDF5 is best suited for storing "heavy" scientific data. Integrating and using both XML and HDF5 for data archiving offers the best solution for data providers and consumers to share information for computational and scientific purposes.

Advances in quantum chemistry in the 1990's have resulted in improvements in the modeling of molecules and in calculating energies, transition structures, and modes of vibration, resulting in the capability to approximate vapor-phase infrared spectral signatures of chemicals using ab-inito calculations. These approaches provide a means of deriving signatures of vapor-phase chemicals, or gases, whose spectra may be too difficult to measure through traditional means. The purpose of this paper will be to review the limitations and accuracies in the modeling of spectral signatures using different methods and basis sets. Comparisons will be made between calculated signatures and laboratory measured signatures.

In nighttime overcast conditions with a new moon (near-total darkness), typical light levels may only reach 10-2-10-4 lux. As such, standard CCD/CMOS video cameras have insufficient sensitivity to capture useful images. Third generation night vision cameras (Gen III NV) are the state-of-the-art in terms of imaging clarity and resolution at this light level, but rely on green or green/yellow phosphors to produce monochromatic images while true color information is lost. More recently, low-light color video cameras have become commercially available which are purportedly able to produce truecolor images at rates of 15-30 frames per second (fps) in near-total darkness without loss in clarity. This study determined if the sensitivities of two low-light color video cameras, Toshiba's IK-1000 EMCCD and Opto-Knowledge System's (OKSI) True Color Night Vision (TCNV) cameras are comparable to current Gen II/III NV technology. NRL, in a joint effort with NSWC Carderock Division, quantified the effectiveness of these cameras in terms of objective laboratory characterization and subjective field testing. Laboratory tests included signal-to-noise (S/N), spectral response, and imaging quality at 2, 15, and 30 frames per second (fps). Field tests were performed at 8, 15, and 30 fps to determine clarity and color composition of camouflaged human subjects and stationary objects from a set number of standoff distances under near-total darkness (measured at 10-8-10-10 W/cm2 sr @ 650nm). Low-light camera video was qualitatively compared to imagery taken by Stanford Photonics Mega-10 Gen III Night Vision Scientific and Tactical Imagers under identical conditions.

Hyperspectral sensors are relatively underexploited tools for geothermal resource exploration. However, both short wave infrared (SWIR) and long wave infrared (LWIR) hyperspectral sensors have demonstrated the potential to play a much more significant role as geologists continue to seek innovative exploration technology to reduce the exploration risk. Exploration managers are becoming familiar with hyperspectral data and derived imagery. The data derivatives are used in the early stages of a phased exploration approach to establish a better understanding of the regional structural setting, and allow the project geologist to optimize field-based exploration methods such as seismic and drilling. However, there is more opportunity for researchers and geologists to expand on the ways that hyperspectral data can provide clues about geothermal systems, especially hidden or "blind" systems. Characterization of surface temperature measurements, development of new target mineral spectra, and recognizing regional trends are some examples of areas where more knowledge and experience will result in more robust data interpretation.

Imaging spectrometry (also known as "hyperspectal imagery", HSI) data are well established for detailed mineral mapping from airborne and satellite systems. Overhead data, however, have substantial additional potential when used together with ground-based HSI measurements. An HSI scanner system was used to acquire airborne data, outcrop scans, and to image boxed drill core and rock chips at approximately 6nm nominal spectral resolution in 360 channels from 0.4 - 2.45 micrometers. Analysis results using standardized hyperspectral methodologies demonstrate rapid extraction of representative mineral spectra and mapping of mineral distributions and abundances. A case history highlights the capabilities of these integrated datasets for developing improved understanding of relations between geology, alteration, and spectral signatures in three dimensions.

A variety of crude oils and refined petroleum products were applied to ten common terrestrial substrates with the goal of developing a set of representative reflectance spectra for hydrocarbon-substrate combinations. Similar to previous studies, each hydrocarbon darkened the substrates and produced hydrocarbon absorption features near 1200, 1690-1770, and 2270-2400 nm, along with a host of other minor features in the VIS/NIR/SWIR portion of the spectrum. Some substrate absorption features interfered with hydrocarbon absorptions, complicating spectral signatures. The reflectance spectra varied directly with the amount of liquid on the substrate. Liquid-saturated samples were left to age and regularly re-measured, establishing a relationship between evaporative loss for volatile and semivolatile products and sample reflectance. The results outline temporal windows of opportunity and minimum detection thresholds for volatiles. They also provide a means for remotely distinguishing 1) water from petroleum products on some substrates and 2) some similar hydrocarbons from one another based on their volatility.

The signatures of turbulence in atmospheric laser propagation are examined, with a particular focus on the effects of non-Kolmogorov turbulence on laser scintillation and phase fluctuations. Non-Kolmogorov properties of the atmospheric index-of-refraction spectrum are outlined, and it is shown that it may be possible to reproduce these features through broadband power-law forcing of the velocity and temperature fields in turbulent flows. Numerical simulations of homogeneous isotropic turbulence subjected to power-law forcing are used to motivate a spectral model for the kinetic energy, which is then extended to address power-law forcing of passive scalars such as the temperature. A modeled non-Kolmogorov index-of-refraction spectrum for power-law forced turbulence is proposed, where the model spectrum consists of standard Kolmogorov and forcing-dominated contributions. This form could reproduce the experimentally observed signatures of atmospheric turbulence on laser propagation and it may provide insights into the origins of non-Kolmogorov turbulence in the atmosphere.

In this work, a free-space infrared communications system is described. The system has the capability of using previously captured scintillation data and introducing the effects onto the bench-top system. This effectively acts as a scintillation simulator which emulates an optical link that is effected by the weather and various physical conditions at the time of transmission. The method used for scintillation simulation is described. The transmission method of the system is a hybrid combination of traditional frequency modulation (FM) and optical amplitude modulation (OAM) combined with Multiple Quantum Well (MQW) Modulating Retroreflector (MRR) technology. The result has produced a robust, low power system that is capable of transmitting real-time audio information with high clarity along a channel that accurately simulates the atmospheric effects of scintillation. The system is capable of transmitting along a link of several kilometers, depending specifically on the characteristics of the interrogator and sensor components chosen for the system.