We describe an optical bench design that efficiently projects broadband infrared energy from a supercontinuum (SC) source onto an extended target and collects the backscattered return. The optical system enables standoff spectral measurements of surfaces at up to 15 m away with a continuous spectral range from 3.6 to 11 μm. The goal of the optical system is to maximize energy transmission and capture under various fundamental and practical constraints imposed by the SC fiber laser source (broad spectrum, high divergence exiting the fiber), spectrometer (light collimation), detectors (étendue limits), and the overall system (size, weight, and cost). The design effort led to an all-reflective, off-axis configuration for both the transmitter and receiver, using custom-designed enhanced gold surfaces. We are able to successfully capture >90 % of the source energy and transmit it with low loss to the remote target while maintaining a nearly diffraction-limited Gaussian beam. The receiver telescope matches the detector étendue while providing uniform (±2.5 % ) off-axis signal collection over the target area to enable scanning of the SC laser spot.
We measure and simulate the diffuse scattering return from sparse particles of RDX, caffeine, and acetaminophen deposited on substrates consisting of smooth aluminum, silicon, and glass substrates at a distance of 3.6 m using a mid-infrared supercontinuum FTIR sensor. The measured spectra show that molecular fingerprint regime contains reflectance peaks that can be used for chemical identification and the mid-wave infrared provides information for target orientation. Furthermore, we demonstrate that spectra obtained using our supercontinuum FTIR sensor can be accurately simulated using a Bobbert-Vlieger model. The Bobbert-Vlieger model can then be used to create a library that can account for the trace chemical, underlying substrate, and target orientation, for stand-off chemical identification.
We demonstrate a prototype sensor capable of measuring specular and diffuse reflectance spectra from samples 3.6 m away. The sensor utilizes mid-wave to long-wave infrared supercontinuum light coupled into a rotational FTIR spectrometer to actively probe remote samples. We measure the diffuse reflectance of acetaminophen at 41.77 μg/cm2 on a glass substrate and find that a modified Bobbert-Vlieger analysis can estimate the effects of particle size distribution on return spectra. We find that the measured return from stand-off particulate measurements depends not only on the chemical identity, but also the size and distribution of particles on the substrate.
Hyperspectral imaging systems are currently used for numerous activities related to spectral identification of
materials. These passive imaging systems rely on naturally reflected/emitted radiation as the source of the
signal. Thermal infrared systems measure radiation emitted from objects in the scene. As such, they can
operate at both day and night. However, visible through shortwave infrared systems measure solar illumination
reflected from objects. As a result, their use is limited to daytime applications. Omni Sciences has produced
high powered broadband shortwave infrared super-continuum laser illuminators. A 64-watt breadboard system
was recently packaged and tested at Wright-Patterson Air Force Base to gauge beam quality and to serve as a
proof-of-concept for potential use as an illuminator for a hyperspectral receiver. The laser illuminator was placed
in a tower and directed along a 1.4km slant path to various target materials with reflected radiation measured
with both a broadband camera and a hyperspectral imaging system to gauge performance.
A fundamental limitation of current visible through shortwave infrared hyperspectral imaging systems is the dependence on solar illumination. This reliance limits the operability of such systems to small windows during which the sun provides enough solar radiation to achieve adequate signal levels. Similarly, nighttime collection is infeasible. This work discusses the development and testing of a high-powered super-continuum laser for potential use as an on-board illumination source coupled with a hyperspectral receiver to allow for day/night operability. A 5-watt shortwave infrared supercontinuum laser was developed, characterized in the lab, and tower-tested along a 1.6km slant path to demonstrate propagation capability as a spectral light source.
Passive infrared spectral sensors (7-14 um) measure brightness temperature along a line of sight, and from these
measurements the presence of a vapor cloud is deduced. How important are atmospheric temperature fluctuations due to
turbulence on the detection of vapors? We developed a stochastic simulation that uses the MODTRAN program to
explore this question. We were surprised to find that although temperature brightness fluctuations are not insignificant
compared to state-of-the-art sensor's noise (modeled as uncorrelated white noise) the effect on detection was very small
because turbulence noise is spectrally correlated and thus its effect was largely removed with a regression algorithm. In
this work we do not address the detection limit due to atmospheric interferences whose effect on detection limit may is
severe.
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.
A data collection experiment was performed in November of 2003 to measure aerosol signatures using multiple sensors, all operating in the long-wave infrared. The purpose of this data collection experiment was to determine whether combining passive hyperspectral and LIDAR measurements can substantially improve biological aerosol detection performance. Controlled releases of dry aerosols, including road dust, egg albumin and two strains of Bacillus Subtilis var. Niger (BG) spores were performed using the ECBC/ARTEMIS open-path aerosol test chamber located in the Edgewood Area of Aberdeen Proving Grounds, MD. The chamber provides a ~ 20' path without optical windows. Ground truth devices included 3 aerodynamic particle sizers, an optical particle size spectrometer, 6 nephelometers and a high-volume particle sampler. Two sensors were used to make measurements during the test: the AIRIS long-wave infrared imaging spectrometer and the FAL CO2 LIDAR. The AIRIS and FAL data sets were analyzed for detection performance relative to the ground truth. In this paper we present experimental results from the individual sensors as well as results from passive-active sensor fusion. The sensor performance is presented in the form of receiver operating characteristic curves.
The HiSPEC instrument was designed to examine the potential for passive detection of sub-lethal concentrations of toxic materials and to test the potential for passive indication of biological agent in air. HiSPEC has been operating since 1999, and after substantial laboratory characterization, taken to the field several times for successful trials against known remote targets. Some subtle differences between laboratory and field performance have been diagnosed for the first time with the aid of HiSPEC's precise internal sampling system. Results of these tests may have implications for improving less sensitive passive field systems. Some recent field data is presented to indicate ultimate potential.
The Chemical Imaging System (CIS) is a small, high-speed long-wave infrared (8 - 12 micrometers ) imaging spectrometer which is currently under development by the United States Army. The fielded system will operate at 360 scans per second with a large format focal-plane-array. Currently, the CIS uses the TurboFT FTS in conjunction with a 16-pixel direct-wired HgCdTe detector array. The TurboFT spectrometer provides high-speed operation in a small, lightweight package. In parallel to the hardware development, an algorithm and software development effort is underway to address some unique features of the CIS. The TurboFT-based system requires a non-uniform sampling Fourier transform algorithm in order to preserve signal fidelity. Also, the availability of multiple pixels can be exploited in order to improve the interference suppression capabilities of the system by allowing the detection and identification algorithm to adapt its parameters to the changing background. Due to the enormous amount of data generated, the signal processing must proceed at very high rate. High-speed computers operating with a parallel architecture are required to process the data in real time. This paper describes the current CIS bread box system. It includes some field measurement results followed by a discussion of the issues and challenges associated with meeting the design goals set for the program.
The Chemical Imaging System (CIS) is a small, high-speed long-wave infrared (8-12 micrometers ) imaging spectrometer which is currently under development by the United States Army. The fielded system will operate at 360 scans per second with a large format focal-plane-array. The CIS, which is currently at the exploratory development stage, is scheduled for transition to engineering development in 2005. Currently, the CIS uses the TurboFT FTS in conjunction with a 16-pixel direct-wired HgCdTe detector array. The TurboFT spectrometer provides high-speed operation in a small, lightweight package. In parallel to the hardware development, an algorithm and software development effort is underway to address some unique features of the CIS. The TurboFT-based system requires a non-uniform sampling Fourier transform algorithm in order to preserve signal fidelity. Also, the availability of multiple pixels can be exploited in order to improve the interference suppression capabilities of the system by allowing the detection and identification algorithm to adapt its parameters to the changing background. Due to the enormous amount of data generated, the signal processing must proceed at very high rate. High-speed computers operating with a parallel architecture are required to process the data in real time. This paper describes the current CIS bread box system. It includes some field measurement results followed by a discussion of the issues and challenges associated with meeting the design goals set for the program.
KEYWORDS: Clouds, Aerosols, Signal to noise ratio, Sensors, Atmospheric particles, Black bodies, Remote sensing, Infrared sensors, Information operations, Refractive index
We present a simple scaling of the SNR plots for the minimum required SNRfor detecting the emission
from an aerosol cloud. The required SNR for the detection of aerosol thermal emission is quite high, in the
order of i03 to iO' (depends on the temperature difference and the depth of the cloud) but can be achieved
with state of the art sensors equipped with large apertures and utilizing sufficient averaging.
Passive standoff detection of chemical warfare (CW) agents is currently achieved by remote sensing infrared spectrometry in the 8 - 12 micrometer atmospheric window with the aid of automatic spectral analysis algorithms. Introducing an imaging capability would allow for rapid wide-area reconnaissance and mapping of vapor clouds, as well as reduce false alarms by exploiting the added spatial information. This paper contains an overview of the CW agent standoff detection problem and the challenges associated with developing imaging LWIR hyperspectral sensors for the detection and quantification of vapor clouds, as well as a discussion of spectral processing techniques which can be used to exploit the added data dimensionality.
KEYWORDS: Chemical analysis, Absorption, Sensors, Clouds, Simulation of CCA and DLA aggregates, Black bodies, Multispectral imaging, Fabry–Perot interferometers, Imaging spectroscopy
The US Army is currently developing a Chemical Imaging Sensor for the wide-area detection and identification of chemical warfare agents. As part of this effort, LWIR multispectral imaging data was collected with the Adaptive Infrared Imaging Spectroradiometer. Laboratory experiments were conducted using chemical agent simulants, including DMMP and DIMP at different vapor concentration levels. The resulting 14-band images were analyzed by a least squares approach as well as by Convex Cone Analysis (CCA). Both analyses demonstrate the potential of multispectral chemical imaging for wide-area standoff detection. In addition, it is shown that CCA is capable of detecting the presence of spectrally active chemical vapors and estimating their absorbance spectrum without any prior knowledge about the presence or composition of the chemical vapors.
We present a method for the automatic, unsupervised detection of spectrally distinct targets from the background using hyperspectral imaging. The approach is based on the concepts of projection pursuit (PP) and unsupervised orthogonal subspace projection (UOSP). It has the advantage of not requiring any prior knowledge of the scene or the objects' spectral signatures. All information is obtained from the data. First, PP is used to both reduce the data dimensionality and locate potential targets. Then, UOSP suppresses the signatures from undesired objects or interferers that cause false detections when a spectral filter is applied. The result is a set of gray scale images where objects belonging to the same spectral class are enhanced while the background and other undesired objects are suppressed. This method is demonstrated using data from the Hyperspectral Digital Imagery Collection Experiment (HYDICE).
Principal Components Analysis is very effective at compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of signal variance. Unfortunately, information content in hyperspectral images does not always coincide with such projections. We propose the application of Projection Pursuit, which seeks to find a set of orthogonal projections that are 'interesting' in the sense that they deviate from the Gaussian distribution assumption. Once these projections are obtained, they can be used for image compression, segmentation, or enhancement for visual analysis. To find these projections we follow a 2-step iterative process where we first search for a projection that maximizes a projection index based on the divergence of the projection's estimated probability distribution from the Gaussian distribution, and then reduce the rank by projecting the data onto the subspace orthogonal to the previous projections. To find the projection that maximizes the index, a novel approach is taken which does not use an optimization algorithm, but rather searches for a solution by obtaining a set of candidate projections from the data and choosing the one with the highest projection index. This method is shown to work with simulated examples as well as data for the Hyperspectral Digital Imagery Collection Experiment.
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