Terahertz Spectroscopy has been recently introduced as a promising technique for the collection of signature data in transmission spectra of biological materials including warfare agent simulants. To characterize material rather than sample, it is always desirable to obtain the material's optical properties as functions of frequency. In this work, we present results from parallel measurements of reflection and transmission spectra of biological molecules to enable detailed and direct calculation of refractive index and absorption coefficient spectra in the terahertz gap. DNA samples from herring and salmon as well as samples of Ovalbumin and Bacillus Subtillus spores have been characterized. The technique for simulation is described. Reflection spectra reveal resonance features similar to those demonstrated earlier for transmission, thereby affirming molecular vibrational modes in biological materials. The dispersion of refractive index and absorption coefficient is demonstrated within the Terahertz gap of 10 cm^-1 to 25 cm^-1.

By directly measuring the absorption spectrum of the chemicals in the vapor phase, we are able to fingerprint molecules. This process is made possible by frequency-tuning a widely-tunable THz source developed by us recently. In the THz region, we have demonstrated that we can use a set of the absorption peaks due to rotational transitions to truly fingerprint chemical species.

In this work, we present initial results of time-domain Fourier transform spectroscopy in the THz frequency range (from 0.5 to 4 THz) of bio-agent simulants. Also shown are spectra acquired with conventional FT-IR spectroscopy. The potential promise and limitations of the technique will be discussed for stand-off detection of biological threat agents.

Terahertz Time domain spectroscopy (THz -TDS ) can provide the optical response of a medium in both amplitude and phase. We show that such capability can enable a detail analysis of optical properties around a resonance regime. Such study is important for standoff detection of explosive material where numerous absorption peaks exist. We proposed a model where one can synthesize the optical properties with THz-TDS around the resonance regimes.

Terahertz Time domain spectroscopy (THz -TDS ) can provide the optical response of a medium in both amplitude and phase. We show that such capability can enable a detail analysis of optical properties of RDX sample. Such study is important for standoff detection of presence of RDX sample, where a detail analysis is difficult if not possible due to a complicated system involved and multiple effects involved. We proposed a match filter method for detection of RDX inside or behind a barrier.

Resonant tunneling diodes (RTDs) are ultra-small semiconductor devices that have potential as very high frequency oscillators. To describe the electron transport within these devices, the Wigner-Poisson Equations are used. These equations incorporate quantum mechanics to describe how the electron distribution changes in time due to kinetic energy, potential energy, and scattering effects. To study the RTD, we apply numerical continuation methods to calculate the steady-state electron distribution as the voltage difference across the RTD varies. To implement the continuation methods, the RTD simulator is interfaced to LOCA (Library of Continuation Algorithm), a software library that is a part of Sandia National Laboratories' parallel solver package, Trilinos. With more sophisticated numerical solvers, we are able to calculate solutions on finer grids that were previously too computationally intensive. This is very important to allow for detailed studies of correlation effects which may dramatically influence oscillatory behavior in RTD-based devices. The more accurate results derived from this work reveal new physical effects that were absent in prior studies. Hence, these physics-based and more refined numerical simulations will provide new insights and greatly facilitate the future optimization of RTD-based oscillator sources and thus has important relevance to THz-frequency-regime based spectroscopic sensing technology.

This paper presents the design of a millimeter-wave Fourier transform spectrometer based on microstrip transmission line circuits and PIN diode switches. The working frequency for this design is in the range of 40-70 GHz which is potentially useful for measurement of absorption spectra of biological materials. Simulation results demonstrate the feasibility of this design and indicate that a frequency resolution of 6 GHz is achievable.

The spin dependent Wigner function is implemented to obtain the IV characteristics of a double barrier resonant tunneling diode with DMS layers. The structure distinguishes between spin-up and spin-down carriers, each of which experiences resonance at different magnetic field dependent bias levels. The results demonstrate the magnetic field dependence of the IV characteristics and illustrate the magnetic field dependence of relative spin-up and spin-down carriers.

Making use of fabrication technology commonly employed in the manufacture of liquid crystal and semiconductor devices, but not previously applied to vacuum devices, the diamond-based backward wave oscillator (BWO) provides a miniature, energy efficient, electronically tunable and mass producible signal source in the sub mm wavelength regime. Fabricated within a shell of chemical vapor deposited (CVD) diamond for mechanical and thermal robustness, the BWO employs a novel biplanar interdigital slow wave circuit, which will be manufactured by utilizing a process developed at Genvac. Conventional silicon fabrication technology is used to form a negative of the desired structure, which serves as a mold for the deposition of the diamond. The diamond structure is then selectively metallized. The structure is formed in two halves and then accurately positioned and bonded using techniques routinely employed in the fabrication of liquid crystal displays. The device has been modeled extensively, and designs of the slow wave circuit, electron gun and collector for operation at 300 and 600 GHz have been completed. Fabrication of the 300 GHz device is in progress. It is estimated to weigh 29 gm, and, for operation over a 10% tuning range, the minimum output power is predicted to be 18 mW.

The utility of infrared spectroscopy for monitoring and early warning of accidental or deliberate chemical releases to the atmosphere is well documented. Regardless of the monitoring technique (open-path or extractive) or weather the spectrometer is passive or active (Fourier transform or lidar) a high quality, quantitative reference library is essential for meaningful interpretation of the data. Pacific Northwest National Laboratory through the support of the Department of Energy has been building a library of pure, vapor phase chemical species for the last 4 years. This infrared spectral library currently contains over 300 chemicals and is expected to grow to over 400 chemicals before completion. The library spectra are based on a statistical fit to many spectra at different concentrations, allowing for rigorous error analysis. The contents of the library are focused on atmospheric pollutants, naturally occurring chemicals, toxic industrial chemicals and chemicals specifically designed to do damage. Applications, limitations and technical details of the spectral library will be discussed.

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.

Physical Sciences Inc. (PSI) has developed an imaging sensor for remote detection of natural gas (methane) leaks. The sensor is comprised of an IR focal plane array-based camera which views the far field through a rapidly tunable Fabry-Perot interferometer. The interferometer functions as a wavelength-variable bandpass filter which selects the wavelength illuminating the focal plane array. The sensor generates 128 pixel x 128 pixel 'methane images' with a spatial resolution of 1 m (>100 x 100 pixel field-of-view). The methane column density at each pixel in the image is calculated in real time using an algorithm which estimates and compensates for line-of-sight atmospheric transmission. The compensation algorithm incorporates range-to-target as well as local air temperature and humidity. System tests conducted at 200 m standoff from sensor to leak location indicate probability of detection >90% for methane column densities >1000 ppmv-m and >2K thermal contrast between the air and the background. The probability of false alarm is <0.2% under these detection conditions.

A selection of field trial results on the passive standoff detection by differential FTIR radiometry with the CATSI sensor is presented. This selection covers the seven-year development period (1998-2004) of the CATSI project. The results obtained with the CATSI instrument at two major field trials in Kansas (1998) and Nevada (2001) have shown the successful passive standoff detection of a number of chemical vapors at short and medium ranges of 100 m and 1.5 km, respectively. In particular, the detection method has been used at short range (100m) to map the column amounts of a methanol plume with an estimated uncertainty of the order of 15 - 30%. At medium range (1.5 km), the measurement technique has been successfully used to detect and identify low, medium and high concentrations of vapor mixtures of DMMP and SF6 but appears to have limited quantification capabilities in its original form. At long range, CATSI has successfully measured SF6 gas amounts at the 5.7-km range of DRDC Valcartier. The passive standoff detection of liquid contaminants on surfaces was tested with encouraging results. These results indicate that liquid contaminant agents deposited on high-reflectivity surfaces can be detected, identified and possibly quantified with passive sensors. For low-reflectivity surfaces, the presence of contaminants can usually be detected; however, their identification based on simple correlations with the absorption spectrum of the pure contaminant is not possible. In a field trial (Dugway Proving Ground, 2002) on the standoff detection of bio-aerosols, CATSI has detected large amounts of BG at ranges of up to 3 km. Recent field measurements for a standoff distance of 60 m suggests that the gas constituent ratios of complex mixtures can be properly retrieved from passive spectral measurements performed at 8 cm^-1. These results from field experiments clearly show the relevance of the CATSI approach for the passive standoff detection by differential FTIR radiometry.

Physical Sciences Inc. (PSI) has recently demonstrated near real-time visualization of chemical vapor plumes via LWIR imaging Fabry-Perot Spectrometry. Simultaneous viewing of the plume from orthogonal lines-of-sight enables estimation of the 3-D plume concentration profile via tomographic analysis of the 2-D 'chemical images' produced by each spectrometer. This paper describes results of field experiments where a controlled release of sulfur hexafluoride (SF₆) was viewed by two Adaptive Infrared Imaging Spectroradiometers (AIRIS) located ~1 km from the plume release point. The PSI tomographic algorithm is capable of generating 3-D density distributions of the chemical cloud that are consistent with atmospheric model predictions even in the extreme limitation of using only two sensors viewing the chemical plume. Each AIRIS unit provides a 64 pixel x 64 pixel image with an angular resolution of ~5.5 mrad/pixel. Each AIRIS was configured to provide continuous coverage of the 10.0-10.8 micrometer spectral region at 6-8 cm^-1 spectral resolution and exhibits a noise equivalent spectral radiance of ~2 micrometer W/(cm² sr micrometer).

Since 1997, Defence Research and Development Canada - Valcartier (DRDC Valcartier) has been developing a Fourier-transform infrared (FTIR) spectrometer for the passive standoff detection of chemical clouds. This instrument, referred to as the Compact ATtmospheric Sounding Interferometer (CATSI), is a double-beam optically balanced FTIR spectrometer operating in the thermal infrared. The double-beam operation mode capability of the instrument is used to suppress the unwanted clutter radiance from the target's background. A methodology for determining the noise equivalent spectral radiance (NESR) of the CATSI instrument in laboratory conditions is presented, along with results using both the double-beam configuration, and a single-beam configuration in which the reference input port is blocked by a liquid Nitrogen-cooled patch, in order to simulate a standard FTIR spectrometer. NESR is found to be depart from the theoretical diminution with integration time due to measurement drifts, which persist even using frequent calibrations of the instrument. NESR evaluation is performed on the real and imaginary part of calibrated measurements after drift correction.

Standoff detection, identification and quantification of chemicals require sensitive spectrometers with calibration capabilities. Recent developments in LWIR focal plane arrays combined with the mastering of Fourier-Transform Spectrometer technology allow the realization of an imaging spectrometer specifically designed for chemical imaging. The spectral and radiometric calibration of the instrument enables the processing of the data to detect the chemicals with spectral signatures in the 8-12 μm region. Spectral images are processed and the contrast between different pixels is used to map the chemicals. Telops has built a field-portable instrument. This paper presents some details about the design of this state-of-the-art sensor. Performance and test results are also presented along with results from a field test.

Over the past year we have initiated a study into the passive standoff detection of radiological materials by Fourier-transform infrared (FTIR) radiometry. The preliminary work has shown that a number of radiological materials have an infrared signature in the 8-14 micron thermal infrared region. As well, through a series of simulations using the MODTRAN4 atmospheric transmission model, we have shown that these materials may potentially be detected at standoff distances of several kilometres. In this paper we present some results of our current work that focus on recent new measurements of spectral signatures, including the uranium oxides UO₂ and UO₃. Preliminary results from a measurement campaign held at Defence Research and Development Canada-Valcartier this year are also presented which address the passive detection of radiological materials in the field at standoff distances of 10-60 m. These results are analysed in view of determining the potential for measuring nuclear products with a passive standoff FTIR technique.

For a passive spectral sensor, the temperature difference (DT) that exists between a chemical cloud and the background scene is of prime importance because it is linked to the radiative contrast of the target. The larger DT, the better the radiative contrast and the more accurate is the detection and identification, of the cloud. This paper establishes statistics on realistic air-soil DT to be used to estimate the detection performance of passive spectral sensors in a variety of scenarios and environments. To this end, an analysis of the air-soil DT is presented for five locations around the world. The results of the analysis indicate that the statistics of the air-soil absolute DT are similar from one location to another. The average statistics over the five locations show a mean absolute air-soil DT of 3.5 °C and a median of 2.8 °C. An absolute air-soil DT of less than one degree Celsius occurs less than 14% of the time on the average. This suggests that, on average, air-soil temperature contrasts should yield good detection probabilities 86% of the time.

The development of hyperspectral imaging instruments designed for water quality assessment, such as the DLR Reflective Optics System Imaging Spectrometer (ROSIS), has created a need for methods which are able to infer water quality parameters of turbid inland waters, and to use those parameters as indicators for water quality. It has been reported that the irradiance reflectance and, subsequently, the radiance collected by the sensor in such scenario is usually the result of an intimate mixture of sub-pixel components. As a result, the commonly used linear mixing model may not be appropriate to describe materials composition. In this work, we develop a nonlinear neural network-based algorithm for estimating water constituent concentrations, with special emphasis on the detection of chemical substances provided by agricultural and industrial sources. The proposed neural network architecture consists of a modified multi-layer perceptron (MLP) whose entries are determined by a linear function activation provided by a Hopfield neural network algorithm (HNN). The combined HNN/MLP supervised model has been used to estimate the concentration of water constituents by training the MLP with ground spectra of nitrogen salts, which are commonly used in extensive agricultural farms. Such spectra were collected using a Minolta spectro-photometer. The model was calibrated in our laboratory by using mixtures of water and nitrogen salt in different proportions. Hyperspectral images collected by the ROSIS imaging spectrometer over the Guadiloba reservoir in Cáceres, SW Spain, are also used in this study to estimate the concentration of nitrogen salts in turbid inland waters.

In this work an Unsupervised Neural Computing Model formed by two neural networks is presented: a Self-Organizing Map (SOM) Network and a Hopfield Recurrent Neural Network (HRNN). The first network extracts the endmembers found in the image, analyzing each pixel, and the second network gets the endmember abundances for each pixel in the image. One of the application fields of the proposed methodology is the water quality analysis. In order to study the behaviour of the proposed model, simulation methods have been used to generate hyperspectral signatures from the water spectra obtained in the laboratory. Such data are used for the training and testing of the network. The first sub-network extracts, from the datasets, the endmembers that are used as training patterns in the second one, that provides the matching abundances. The results obtained here will be applied to the treatment of the hyperspectral image Cáceres ES-4, got by the sensors DAIS and ROSIS, from Guadiloba reservoir.

Hyperspectral images have high spectral resolution that enables accurate object classification. But its vast data volume brings about problems in data transmission and data storage. How to reduce the data volume while keeping the important information for the following data analysis is a challenging task. Principal Components Analysis (PCA) is a typical method for data compression, which re-arranges image information into the first several principal component images in terms of variance. But we know variance is not a good criterion to rank images. Instead, signal-to-noise ratio (SNR) is a more reasonable criterion, and the resulting PCA is called noise adjusted PCA (NAPCA). We also know that interference is a very serious problem in hyperspectral images, and have proposed signal-to-interference-plus-noise (SINR) as a ranking criterion. The resulting PCA is referred to as interference and noise adjusted PCA (INAPCA). In this paper, we will investigate the NAPCA and INAPCA to hyperspectral image compression. The focus is the analysis of their impacts on the following data exploitation (such as detection and classification). It is expected that using NAPCA and INAPCA higher detection and classification rates can be achieved with a comparable or higher compression ratio, compared to PCA-based compression.

Least square unmixing approach has been successfully applied to hyperspectral remotely sensed images for subpixel target detection. It can detect target with size less than a pixel by estimating its abundance fraction resident in each pixel. In order for the this approach to be effective, the number of bands must be larger than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It is known as band number constraint (BNC). Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified might be greater than the number of bands. In order to relax this constraint, an extension of the least square approach is presented. With a set of least square filters that are nonlinearly combined, endmember detection for multispectral images can be realized. Furthermore, to detect targets in unknown background is a greater challenge. That is the well known Automatic Target Recognition (ATR) programs. In this paper, we also proposed a Multispectral Target Generation Process (MTGP) that will automatic search for potential targets in the image scene. The effectiveness of the proposed method is evaluated by SPOT images. The experimental results show significantly improves in classification performance than Orthogonal Subspace Projection (OSP) and Automatic Target Detection and Classification Algorithm (ATDCA).

Target detection is one of the major tasks in hyperspectral image analysis. Constrained Energy Minimization (CEM) is a popular technique for target detection. It designs a finite impulse response filter in such a manner that the filter output energy is minimized subject to a constraint imposed by the desired target of interest. It is particular useful when only the desired target signature is available. When those undesired signatures to be eliminated are also known, Target Constrained Interference Minimization Filter (TCIMF) can be used to minimize the output of undesired signatures to further improve the performance. It has been demonstrated that TCIMF can better differentiate targets with similar spectral signatures. Both CEM and TCIMF involve the calculation of the data sample correlation matrix R and its inverse matrix R^-1. The function of R^-1 is background suppression. When the target to be detected is very small and embedded at the sub-pixel level, it is difficult to detect it. But if the data sample correlation matrix R can well present the statistics of the background surrounding the pixel containing the object such that R^-1 can well suppress the background, the target may still have a chance to be detected. So in this paper we propose a localized processing technique. Instead of using all the pixels in an image scene to calculate the R, only several lines of pixels near the pixel to be processed are used for the R computation. The preliminary result using an HYDICE image scene demonstrates the effectiveness of such a localized processing technique in the detection of targets at sub-pixel level. Interestingly, in some cases it can also improve the performance of CEM in target discrimination.

Kalman filter has been widely used in statistical signal processing for parameter estimation. Although a Kalman filter approach has been recently developed for spectral unmixing, referred to as Kalman filter-based linear unmixing (KFLU), its applicability to spectral characterization within a single pixel vector has not been explored. This paper presents a new application of Kalman filtering in spectral estimation and quantification. It develops a Kalman filter-based spectral signature esimator (KFSSE) which is different from the KFLU in the sense that the former performs a Kalman filter wavelength by wavelength across a spectral signature as opposed to the latter which implements a Kalman filter pixel vector by pixel vector in an image cube. The idea of the KFSSE is to implement the state equation to characterize the true spectral signature, while the measurement equation is being used to describe the spectral signature to be processed. Additionally, since a Kalman filter can accurately estimate spectral abundance fraction of a signature, our proposed KFSSE can further used for spectral quantification for subpixel targets and mixed pixel vectors, called Kalman filter-based spectral quantifier (KFSQ). Such spectral quantification is particularly important for chemical/biological defense which requires quantification of detected agents for damage control assessment. Several different types of hyperspectral data are used for experiments to demonstrate the ability of the KFSSE in estimation of spectral signature and the utility of the KFSQ in spectral quantification.

There is an immediate need for the ability to detect, identify and quantify chemical and biological agents in water supplies during water point selection, production, storage, and distribution to consumers. Through a U.S. Army sponsored Joint Service Agent Water Monitor (JSAWM) program, based on hand-held assays that exist in a ticket format, we are developing new algorithms for automatic processing of tickets. In previous work, detection of control dots in the tickets was carried out by traditional image segmentation approaches such as Otsu's method and other entropy-based thresholding techniques. In experiments, it was found that the approaches above were sensitive to illumination effects in the camera reader. As a result, more robust, object-oriented approaches to detect the control dots are required. Mathematical morphology is a powerful technique for image analysis that focuses on the size and shape of the objects in the scene. In this work, we describe a novel application of morphological operations in identification of control dots in hand held assay ticket imagery. Such images were pre-processed by a light compensation algorithm prior to morphological analysis. The performance of the proposed approach is evaluated using Receiving Operating Characteristics (ROC) analysis.

SciSat-1, otherwise known as the Atmospheric Chemistry Experiment (ACE), is a Canadian satellite mission for remote sensing of the Earth's atmosphere. It was launched into low Earth orbit (altitude 650 km, inclination 74 degrees) in August 2003. The primary instrument onboard ACE is a high resolution (maximum path difference ± 25 cm) Fourier Transform Spectrometer (FTS) operating from 2.4 to 13.3 microns (750-4100 cm^-1). The satellite also features a dual spectrograph known as MAESTRO with wavelength coverage 280-1000 nm and resolution 1-2 nm. A pair of filtered CMOS detector arrays takes images of the sun at 0.525 and 1.02 nm. Working primarily in solar occultation, the satellite provides altitude profile information for temperature, pressure, and the volume mixing ratios for several dozen molecules of atmospheric interest. Scientific goals for ACE include: (1) understanding the chemical and dynamical processes that control the distribution of ozone in the stratosphere and upper troposphere; (2) exploring the relationship between atmospheric chemistry and climate change; (3) studying the effects of biomass burning in the free troposphere; and (4) measuring aerosols to reduce the uncertainties in their effects on the global energy balance.

The primary objective of the Canadian SciSat-1 mission is to investigate the processes that control the distribution of ozone in the stratosphere. The SciSat-1 satellite consists of two major science instruments; an Atmospheric Chemistry Experiment (ACE) high-resolution Fourier-transform spectrometer (FTS) and an ultraviolet/visible/near-infrared spectrograph. These instruments primarily function in occultation mode; however, during the dark portion of the orbit the Earth passes between the sun and the satellite. This configuration provides the opportunity of acquiring some nadir-view FTIR spectra of the Earth. Preliminary nadir spectra obtained with the ACE FTS are presented and analyzed for methane, ozone and nitrous oxide. Applications of these measurements to the study of global warming and air pollution monitoring are discussed.

One of the gases which potentially can interfere with the remote sensing of gases of military interest is carbon tetrachloride (CCL₄). Carbon tetrachloride is also a strong greenhouse gas with a global warming potential of 4,000. Ground-based, thermal emission measurements of the cold, clear sky have been made showing the v₃ fundamental emission band of carbon tetrachloride (CCL₄) which is located in the 786-806 cm^-1 region. A spectrum of the non-CCl₄ background emission features has been simulated using the FASCD3P line-by-line radiation code with measured radiosonde parameters of pressure, temperature and humidity. The simulated spectrum has been used to extract the CCl₄ thermal emission band from the atmospheric emission spectrum. Troposhperic CCl₄ mixing ratios of 120±20 in 1995 and 135±10 pptv in 2003 were determined from these measurements. In addition, the downward long-wave flux associated with the v₃ emission band of CCl₄ measured at the surface has been estimated to be 0.046 W/m² ± 17%. This flux is about one third and one fifth of that corresponding to the chlorofluorocarbons CFC-11 and CFC-12, respectively.

There has been little experimental verification of the radiative forcing from air pollutants under cloudy conditions. This paper reports on the progress which has been made towards validating the predictions of the climate forcing associated with air pollutants. Measurements have been taken over the last three years with a new technique which was developed to measure the greenhouse radiative fluxes from greenhouse gases beneath clouds. These measurements are valuable since there are large spatial and temporal variations in some gases which make it difficult to quantify their climate forcing. As a result of the poor state of knowledge of the radiative forcing associated with prime constituents of smog such as nitric acid or PAN are omitted in the Kyoto protocol for the reduction of greenhouse gases. In our technique, measurements of the surface radiative forcing from the gases below the cloud are taken against the cold black body background of the cloudy sky. Radiative fluxes from ozone, carbon monoxide, nitrous oxide, nitric acid and aerosols have been measured. This technique may have applications to battlefield remote sensing of gases. Our measurements have been made at 44N over all four seasons. In order to further decrease the uncertainty of the tropospheric forcing, many more measurements need to be made at different latitudes and climates.

Measuring soil moisture turns out to be crucial in watershed management. This study presents soil moisture prediction using RADRASAT-1 Synthetic Aperture Radar (SAR) satellite imagery collected in the Choke Canyon Reservoir Watershed (CCRW) in April 2004. Essential radiometric and geometric calibrations to correct the SAR imagery were performed with the aid of Corner Reflectors (CRs). The sensor data obtained after the installation of the corner reflectors in April 2004 showed better spatial accuracy, and consequently improves the correlation between the radar backscatter signals, s0, and the Volumetric Moisture Content (VMC) of the soil in CCRW. Three prediction models were developed for soil moisture projection, which include simple linear regression, multiple linear regression, and genetic programming models. It found that the genetic programming model exhibits overall advantage of soil moisture estimation.

This paper surveys the potential use of hyperspectral imaging technology for standoff detection of chemical and biological agents in terrorism defense applications. In particular it focuses on the uses of hyperspectral imaging technology to detect and monitor chemical and biological attacks. In so doing it examines current technologies, their advantages and disadvantages, and investigates the possible role of hyperspectral imaging for homeland security applications. The study also addresses and provides applicable solutions for several of the potential challenges that currently create barriers to the full use of hyperspectral technology in the standoff detection of likely available chemical and biological agents.