This PDF file contains the front matter associated with SPIE Proceedings Volume 8515, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Toxic industrial chemicals (TICs) represent a major threat to public health and security. Their detection constitutes a real challenge to security and first responder's communities. One promising detection method is based on the passive standoff identification of chemical vapors emanating from the laboratory under surveillance. To investigate this method, the Department of National Defense and Public Safety Canada have mandated Defense Research and Development Canada (DRDC) - Valcartier to develop and test passive Long Wave Infrared (LWIR) hyperspectral imaging (HSI) sensors for standoff detection. The initial effort was focused to address the standoff detection and identification of toxic industrial chemicals (TICs) and precursors. Sensors such as the Multi-option Differential Detection and Imaging Fourier Spectrometer (MoDDIFS) and the Improved Compact ATmospheric Sounding Interferometer (iCATSI) were developed for this application. This paper describes the sensor developments and presents initial results of standoff detection and identification of TICs and precursors. The standoff sensors are based on the differential Fourier-transform infrared (FTIR) radiometric technology and are able to detect, spectrally resolve and identify small leak plumes at ranges in excess of 1 km. Results from a series of trials in asymmetric threat type scenarios will be presented. These results will serve to establish the potential of the method for standoff detection of TICs precursors and surrogates.

The MR-i spectroradiometer can support a wide range of applications from its architecture suited to multiple configurations. Its modular 4-port FTIR spectroradiometer architecture allows the simultaneous use of two different detector modules, direct or differential input(s) and multiple telescopes. In a given configuration, MR-i can combine a MWIR focal plane array and a LWIR focal plane array to provide an extended spectral range from the two imaging sensors. The two detector array modules are imaging the same scene allowing synchronized pixel-to-pixel spectral range combination. In another configuration, MR-i can combine two identical focal plane arrays with different attenuation factors and two interleaved integration times per detector array. This configuration generates four sets of hyperspectral data cubes with different dynamic ranges that can be combined to produce a single hyperspectral cube with unmatched dynamic range. This configuration is particularly well suited for high-speed, high-dynamic range characterization of targets such as aircrafts, flares, and explosions. In a third configuration, named iCATSI, the spectroradiometer is used in differential input configuration to provide efficient optical background subtraction. The iCATSI configuration features an MCT detectors array with spectral cutoff near 14 µm. This extended spectral range and high sensitivity allows the detection and identification of a wide range of chemicals.

The Geostationary Fourier Transform Spectrometer (GeoFTS) is an imaging spectrometer designed for a geostationary orbit (GEO) earth science mission to measure key atmospheric trace gases and process tracers related to climate change and human activity. GEO allows GeoFTS to continuously stare at a region of the earth for frequent sampling to capture the variability of biogenic fluxes and anthropogenic emissions from city to continental spatial scales and temporal scales from diurnal, synoptic, seasonal to interannual. The measurement strategy provides a process based understanding of the carbon cycle from contiguous maps of carbon dioxide (CO₂), methane (CH₄), carbon monoxide (CO), and chlorophyll fluorescence (CF) collected many times per day at high spatial resolution (~2.7km×2.7km at nadir). The CO₂/CH₄/CO/CF measurement suite in the near infrared spectral region provides the information needed to disentangle natural and anthropogenic contributions to atmospheric carbon concentrations and to minimize uncertainties in the flow of carbon between the atmosphere and surface. The half meter cube size GeoFTS instrument is based on a Michelson interferometer design that uses all high TRL components in a modular configuration to reduce complexity and cost. It is self-contained and as independent of the spacecraft as possible with simple spacecraft interfaces, making it ideal to be a “hosted” payload on a commercial communications satellite mission. The hosted payload approach for measuring the major carbon-containing gases in the atmosphere from the geostationary vantage point will affordably advance the scientific understating of carbon cycle processes and climate change.

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the Earth Observing System (EOS) Aqua Spacecraft, launched on May 4, 2002 into a near polar sun-synchronous orbit. AIRS has 2378 infrared channels ranging from 3.7 μm to 15.4 μm and a 13.5 km footprint at nadir. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts for CO₂, CO, SO₂, O₃ and CH₄. AIRS CO₂ climatologies have been shown to be useful for identifying anomalies associated with geophysical events such as El Niño-Southern Oscillation or Madden–Julian oscillation. In this study, monthly representations of mid-tropospheric CO₂ are constructed from 10 years of AIRS Version 5 monthly Level 3 data. We compare the AIRS mid-tropospheric CO₂ representations to ground-based measurements from the Scripps and National Oceanic and Atmospheric Administration Climate Modeling and Diagnostics Laboratory (NOAA CMDL) ground networks to better understand the phase lag of the CO₂ seasonal cycle between the surface and middle troposphere. Results show only a small phase lag in the tropics that grows to approximately two months in the northern latitudes.

In this paper we discuss an algorithm developed to detect re in high-resolution commercial multispectral imagery. We discuss the utility of such algorithms and present to the reader the challenges in developing these types of algorithms. A thorough description of the algorithm is presented along with the results of its experimental performance measurements.

Southern California experienced some of the largest wildfires ever seen in 2003 and 2007. The Cedar fire in 2003 resulted in 2,820 lost structures and 15 deaths, and the Witch fire in 2007 resulted in 1,650 lost structures and 2 deaths according to the California Department of Forestry and Fire Protection (CAL FIRE). Fighting fires of this magnitude requires every available resource, and an adequate water supply is vital in the firefighting arsenal. Utilizing the fact that many homes in Southern California have swimming pools, firefighters could have access to strategically placed water supplies. The problem is accurately and quickly identifying which residences have actively filled swimming pools at the time of the emergency. The proposed method approaches the problem by employing satellite imagery and remote sensing techniques. Specifically, swimming pool identification is attempted with Spectral Angle Mapper (SAM) on multispectral imagery from the Worldview-2 satellite.

Digital cameras and imaging/non-imaging spectrometers covering the ultraviolet through long-wave infrared wavelengths are readily available and affordable for use by scientists and emergency responders. An important addition that will enhance the value of these precision sensors as tools in the hands of scientists and emergency responders is the capability to produce automated geo-registered or ortho-rectified imagery maps from their data. This paper describes the Mapping System Interface Card (MSIC), a low cost, compact, realtime precision metadata annotator with embedded INS/GPS designed specifically to convert commercial-offthe- shelf (COTS) sensors with Camera Link standard data streams into mapping systems for airborne remote sensing applications.

The detection of underground structures, both natural and man-made, continues to be an important requirement in both the military/intelligence and civil communities. There are estimates that as many as 70,000 abandoned mines/caves exist across the nation. These mines represent significant hazards to public health and safety, and they are of concern to Government agencies at the local, state, and federal levels. NASA is interested in the detection of caves on Mars and the Moon in anticipation of future manned space missions. And, the military/ intelligence community is interested in detecting caves, mines, and other underground structures that may be used to conceal the production of weapons of mass destruction or to harbor insurgents or other persons of interest by the terrorists. Locating these mines/caves scattered over millions of square miles is an enormous task, and limited resources necessitate the development of an efficient and effective broad area search strategy using remote sensing technologies. This paper describes an internally-funded research project of The Aerospace Corporation (Aerospace) to assess the feasibility of using airborne hyperspectral data to detect abandoned cave/mine entrances in a broad-area search application. In this research, we have demonstrated the potential utility of using thermal contrast between the cave/mine entrance and the ambient environment as a discriminatory signature. We have also demonstrated the use of a water vapor absorption line at12.55 μm and a quartz absorption feature at 9.25 μm as discriminatory signatures. Further work is required to assess the broader applicability of these signatures.

Remote sensing or stand-off detection using controlled light sources is a well known and often used technique for atmospheric and surface spatial mapping. Today, ground based, vehicle-borne and airborne systems are able to cover large areas with high accuracy and good reliability. This kind of detection based on LiDAR (Light Detection and Ranging) or active Differential Optical Absorption Spectroscopy (DOAS) technologies, measures optical responses from controlled illumination of targets. Properties that can be recorded include volume back-scattering, surface reflectivity, molecular absorption, induced fluorescence and Raman scattering. The various elastic and inelastic backscattering responses allow the identification or characterization of content of the target volumes or surfaces. INO has developed instrumentations to measure distance to solid targets and monitor particles suspended in the air or in water in real time. Our full waveform LiDAR system is designed for use in numerous applications in environmental or process monitoring such as dust detection systems, aerosol (pesticide) drift monitoring, liquid level sensing or underwater bathymetric LiDARs. Our gated imaging developments are used as aids in visibility enhancement or in remote sensing spectroscopy. Furthermore, when coupled with a spectrograph having a large number of channels, the technique becomes active multispectral/hyperspectral detection or imaging allowing measurement of ultra-violet laser induced fluorescence (UV LIF), time resolved fluorescence (in the ns to ms range) as well as gated Raman spectroscopy. These latter techniques make possible the stand-off detection of bio-aerosols, drugs, explosives as well as the identification of mineral content for geological survey. This paper reviews the latest technology developments in active remote sensing at INO and presents on-going projects conducted to address future applications in environmental monitoring.

We report the characteristics of the Portable Remote Imaging Spectrometer, an airborne sensor specifically designed for the challenges of coastal ocean research. PRISM has high signal to noise ratio and uniformity, as well as low polarization sensitivity. Acquisition of high quality data has been demonstrated with the first engineering flight.

The Ultra-Compact Imaging Spectrometer (UCIS) is a miniature telescope and spectrometer system intended for mapping terrain mineralogy over distances from 1.5 m to infinity with spatial sampling of 1.35 mrad over a 30° field, and spectral sampling of 10 nm in the 600-2500 nm range. The core of the system has been designed for operation in a Martian environment, but can also be used in a terrestrial environment when placed inside a vacuum vessel. We report the laboratory and field calibration data that include spatial and spectral calibration, and demonstrate the use of the system.

A liquid crystal tunable filter (LCTF) with large aperture is developed using PDLC liquid crystal. A small scale imaging spectrometer is established based on this tunable filter. This spectrometer can continuously tuning, or random-access selection of any wavelength in the visible and near infrared (VNIR) band synchronized with the imaging processes. Notable characteristics of this spectrometer include the high flexibility control of its operating channels, the image cubes with high spatial resolution and spectral resolution and the strong ability of acclimation to environmental temperature. The image spatial resolution of each tuning channel is almost near the one of the same camera without the LCTF. The spectral resolution is about 20 nm at 550 nm. This spectrometer works normally under 0-50°C with a maximum power consumption of 10 Watts (with exclusion of the storage module). Due to the optimization of the electrode structure and the driving mode of the Liquid Crystal cell, the switch time between adjacent selected channels can be reduced to 20 ms or even shorter. Spectral imaging experiments in laboratory are accomplished to verify the performance of this spectrometer, which indicate that this compact imaging spectrometer works reliably, and functionally. Possible applications of this imaging spectrometer include medical science, protection of historical relics, criminal investigation, disaster monitoring and mineral detection by remote sensing.

Chemical cloud detection using long-wave infrared (LWIR) hyperspectral-imaging sensors has many civilian and military applications, including chemical warfare threat mitigation, environmental monitoring, and emergency response. Current capabilities are limited by variation in background clutter as opposed to the physics of photon detection, and this makes the statistical characterization of clutter and clutter-induced false alarms essential to the design of practical systems. In this exploratory work, we use hyperspectral data collected both on the ground and in the air to spectrally and spatially characterize false alarms. Focusing on two widely-used detectors, the matched filter (MF) and the adaptive cosine estimator (ACE), we compare empirical false-alarm rates to their theoretical counterparts - detector output under Gaussian, t and t-mixture distributed data - and show that these models often underestimate false-alarm rates. Next, we threshold real detection maps and show that true detections and false alarms often exhibit very different spatial behavior. To exploit this difference and understand how spatial processing affects performance, the spatial behavior of false alarms must be understood. We take a first step in this direction by showing that, although the behavior may `look' quite random, it is not well captured by the complete-spatial-randomness model. Finally, we describe how our findings impact the design of real detection systems.

This paper describes the end-to-end processing of image Fourier transform spectrometry data taken of surrogate tank targets at Picatinny Arsenal in New Jersey with the long-wave hyper-spectral camera HyperCam from Telops. The first part of the paper discusses the processing from raw data to calibrated radiance and emissivity data. The second part discusses the application of a range-invariant anomaly detection approach to calibrated radiance, emissivity and brightness temperature data for different spatial resolutions and compares it to the Reed-Xiaoli detector.

Feature-based imaging spectroscopy methods are effective for identifying materials that exhibit specific well-defined spectral absorption features. As long as a pixel contains a sufficient amount of material so that the absorption retains its predominant shape, a feature-based method can work well. However, there are occasions when a background material can mix with a material of interest, and significantly distort and maybe even remove the absorption. In such cases, the material identification capabilities of these methods are likely to be degraded. This effort proposes an approach to accommodate these conditions. The parameter values to determine fit of an absorption feature are selected to be more tolerant of distortions and the signal contributions of any detected sub-pixel backgrounds are removed by making use of a physically-constrained linear mixing model. This mixing model is used to remove any detected background spectra from the image spectra within the bounding locations of the spectral features. However, an expected consequence of loosening the parameter values and performing sub-pixel subtraction is an increase in false alarms. A statistically-based spectral matched filter is proposed as to reduce these false alarms. We test the individual and combined approaches for identifying full-pixel and sub-pixel Tyvek panels in an experiment using a HyMAP hyperspectral scene with ground truth collected over Waimanalo Bay, Oahu, Hawaii.

A method of incorporating the multi-mixture pixel model into hyperspectral endmember extraction is presented and discussed. A vast majority of hyperspectral endmember extraction methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers. Methods exist to unmix hyperspectral pixels using nonlinear models, but rely on severely limiting assumptions or estimations of the nonlinearity. This paper will present a hyperspectral pixel endmember extraction method that utilizes the bidirectional reflectance distribution function to model microscopic mixtures. Using this model, along with the linear mixture model to incorporate macroscopic mixtures, this method is able to accurately unmix hyperspectral images composed of both macroscopic and microscopic mixtures. The mixtures are estimated directly from the hyperspectral data without the need for a priori knowledge of the mixture types. Results are presented using synthetic datasets, of multi-mixture pixels, to demonstrate the increased accuracy in unmixing using this new physics-based method over linear methods. In addition, results are presented using a well-known laboratory dataset.

A method of incorporating the multi-mixture pixel model into hyperspectral endmember extraction is presented and discussed. A vast majority of hyperspectral endmember extraction methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers. Methods exist to unmix hyperspectral pixels using nonlinear models, but rely on severely limiting assumptions or estimations of the nonlinearity. This paper will present a hyperspectral pixel endmember extraction method that utilizes the bidirectional reflectance distribution function to model microscopic mixtures. Using this model, along with the linear mixture model to incorporate macroscopic mixtures, this method is able to accurately unmix hyperspectral images composed of both macroscopic and microscopic mixtures. The mixtures are estimated directly from the hyperspectral data without the need for a priori knowledge of the mixture types. Results are presented using synthetic datasets, of multi-mixture pixels, to demonstrate the increased accuracy in unmixing using this new physics-based method over linear methods. In addition, results are presented using a well-known laboratory dataset.

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) program objectives are recommended by the NRC as a Tier-1 mission in its January 15, 2007 Earth Science Decadal Survey to be the key component of a future decade-scale, global climate change observing system. The purpose of CLARREO is to make SI-traceable absolute observations sensitive to the most critical, but least understood climate forcing phenomena, responses, and feedbacks. NASA / LaRC is the mission lead as well as the Infrared (IR) instrument suite development lead. The Reflected Solar (RS) instrument lead center has been assigned to GSFC where engineering risk reduction and science calibration demonstration studies are being conducted on flight-like ETUs in anticipation of entry into Phase A. The RS instrument suite (SOLARIS) is composed of multiple all-aluminum, slit-based, push-broom imaging spectroradiometers of nearly identical construction. Each 'box' will be optimized to provide better than 8nm spectral resolution (using multiple detector elements) over a specific spectral band covering the 320-2300nm total range with significant overlaps to aid calibration. Optical design, fabrication, and alignment will provide for 500m nadir resolutions over a full slit field of 100km from an approximately 600km polar orbit greater than 90% of the time. SNRs are likewise required to exceed 33 for λ < 900nm and 25 for λ < 900nm. The maximum radiometric sensitivity to any naturally-occurring polarized scene elements is expected to be between 0.5% - 0.75% for λ < 1000nm and λ <1000nm respectively. The RS suite system will be capable of demonstrating a long-term, spectrally- & spatially-averaged, systematic radiometric error of less than 0.3% (k=2). Coupled with measurements from on-board GPS radio occultation receivers and inherent inter-calibration compatibility with existing and future Earth science and operational missions, these measurements will provide a long-term benchmarking data record for the detection, projection, and attribution of changes to our planet's climate system. The CLARREO Project team successfully completed its Mission Concept Review (MCR) on November 17, 2010 at LaRC with high marks and remains dedicated to the mission and its instruments. However, the launch readiness date (LRD) is yet to be determined pending budget directive updates from the White House along with review of the IR and RS calibration demonstration efforts (extended pre-Phase A).

The point response function (PRF) describes the response of an instrument to radiance from a point within its field of view. The Clouds and Earth Radiant Energy System (CERES) PRF is used together with measurements from the Moderate Resolution Imaging Spectro-radiometer (MODIS) to compute cloud information for each CERES pixel. Knowledge of the point response function (PRF) of CERES is essential to accurately align these data sets. The PRF has been measured for each CERES instrument during ground calibrations. Using in-orbit lunar calibrations, over ten years of data have been compiled for the Flight Models-1 and -2 aboard the Terra satellite and over eight years have been recorded for Flight Models-3 and -4 aboard the Aqua satellite. These data are used to examine the stability of the PRF of these instruments over the duration of their operations-to-date. In-orbit calibrations are taken at a lower scanning rate when compared to ground testing. As a result, these lunar scans provide more precise detector mapping capability due to a higher data sampling rate. Additional instrument performance measurements which can be gleaned are detector sensitivity stability and pointing accuracy.

Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of proposed new observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at NASA/ GSFC and NOAA/AOML in collaboration with Simpson Weather Associates and operational data assimilation centers over the last 27 years. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch, evaluated trade-offs in orbits, coverage and accuracy for space-based wind lidars, and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. In this paper, we summarize applications of global OSSEs to hurricane track forecasting, and current experiments using both global and regional models aimed at both track and intensity forecasting.

We explore how a class of unconventionally polarized beams can be applied to such problems as aerosol polarimetry. One such beam can be formed by the superposition of right and left circular-polarized beams combined in a Twyman-Green Interferometer, and adjusted with linear tilt. Such superposition results in a beam that repeatedly traverses the equator of the Poincaré sphere in one of the beams spatial dimensions, such that an image of light scattered from the beam can yield the phase function of the scatterers without temporal modulation of the input polarization. An equivalent, but more robust method, uses a specially designed Nomarski prism arrangement to introduce a known angular shear between right and left circularly polarized fields.

For Counter Improvised Explosive Devices purposes, main routes including their vicinity are surveyed. In future military operations, small hyperspectral sensors will be used for ground covering reconnaissance, complementing images from infrared and high resolution sensors. They will be mounted on unmanned airborne vehicles and are used for on-line monitoring of convoy routes. Depending of the proximity to the road, different regions can be defined for threat assessment. Automatic road tracking can help choosing the correct areas of interest. Often, the exact discrimination between road and surroundings fails in conventional methods due to low contrast in pan-chromatic images at the road boundaries or occlusions. In this contribution, a novel real-time lock-on road tracking algorithm is introduced. It uses hyperspectral data and is specifically designed to address the afore- mentioned deficiencies of conventional methods. Local features are calculated from the high-resolution spectral signatures. They describe the similarity to the actual road cover and to either roadside. Classification is per- formed to discriminate the signatures. To improve robustness against variations in road cover, the classification results are used to progressively adapt the road and roadside classes. Occlusions are treated by predicting the course of the road and comparing the signatures in the target area to previously determined road cover signa- tures. The algorithm can be easily extended to show regions of varying threat, depending on the distance to the road. Thus, complex anomaly detectors and classification algorithms can be applied to a reduced data set. First experiments were performed for AISA Eagle II (400nm - 970nm) and AISA Hawk (970nm - 2450nm) data

This paper describes about the new design method for hyper-spectral Imaging spectrometers utilizing convex grating. Hyper-spectral imaging systems are power tools in the field of remote sensing. HSI systems collect at least 100 spectral bands of 10~20 nm width. Because the spectral signature is different and induced unique for each material, it should be possible to discriminate between one material and another based on difference in spectral signature of material. I mathematically analyzed parameters for the intellectual initial design. Main concept of this is the derivative of "ring of minimum aberration without vignetting". This work is a kind of analytical design of an Offner imaging spectrometer. Also, several experiment methods will be contrived to evaluate the performance of imaging spectrometer.