The NVESD COMPASS instrument is an airborne dispersive hyperspectral imager that covers the VNIR through SWIR bands and incorporates a real-time data processing system. The processing system consists of a Data Processing Computer (DPC) and an Operator Display/Control Computer (ODC). The high-performance DPC executes real-time sensor calibration and multiple spectral detection algorithms on 13 G4-processors in a Race++ switched backplane. The DPC sends three-band pseudo-color hyperspectral data, high-resolution target chips, and GPS/INS data to the ODC. The ODC outputs a geo-registered display of HSI color imagery with detection cue overlays. The COMPASS detection algorithms, which are particularly well suited to CC&D targeting applications, include the SSRX spectral anomaly detector, the NFINDR/STD spectral unmixing-based anomaly detector, (3) a supervised spectral matched filter (SSMF), and (4) Healey's invariant subspace detector. The DPC airborne component is VME-based in a compact, ruggedized chassis. The COMPASS real-time processor is a second generation system based on NRL-sponsored WarHORSE demonstrations. This paper reviews the DPC system design, capabilities and performance.

More and more hyperspectral sensors are now employing two-dimensional focal plane arrays to simultaneously record the spectra for a line of points on the ground. Since a large number of spectra are obtained simultaneously, the instantaneous data rate can be much higher than that achieved with a flying spot scanner. Unfortunately, the use of more than one detector per band means that there are many new sources of sensor pattern that must be removed during preprocessing. These sources of pattern usually are the limitation to the performance of imaging array spectrometers. One of the more troublesome problems with focal plane arrays is the existence of dead or bad detectors. For an imaging system, the effect of these detectors is removed by interpolation with neighbors. The problem is much more difficult to solve when the array is used as the focal plane in a hyperspectral instrument. If the bad detectors are ignored, the result is a stripe down the image in a particular band. Simple interpolation in the spectral direction can be atempted, but often the interpolation itself is the source of stripes in the image. The effect of inaccurate interpolation is particularly noticeable in the vicinity of atmospheric absorption features, where the spectral variation with wavelength is far from linear. The bad detector is replaced by the average of its two neighboring point in the spectra, which fails to match the proper value for that point. While this value is a better match than the uncorrected detector, the result is still a stripe in the image. This stripe will show up in many hyperspectral analysis operations. One solution is to move the atmospheric compensation step into the preprocessing to remove the rapidly changing spectral features before the bad detectors are removed by interpolation. This would be a rearrangement of the normal division between level 1 and level 2 processing. In this paper an alternative procedure to minimize the effect of bad detectors is discussed. This procedure avoids the atmospheric correction in preprocessing.

To achieve enhanced target discrimination, prototype three- band long wave infrared (LWIR) focal plane arrays (FPA) for missile defense applications have recently been constructed. The cutoff wavelengths, widths, and spectral overlap of the bands are critical parameters for the multicolor sensor design. Previous calculations for sensor design did not account for target and clutter spectral features in determining the optimal band characteristics. The considerable spectral overlap and correlation between the bands and attendant reduction in color contrast is another unexamined issue. To optimize and simulate the projected behavior of three-band sensors, this report examined a hyperspectral LWIR image cube. Our study starts with 30 bands of the LWIR spectra of three man-made targets and natural backgrounds that were binned to 3 bands using weighted band binning. This work achieves optimal binning by using a genetic algorithm approach and the target-to-clutter-ratio (TCR) as the optimization criterion. Another approach applies a genetic algorithm to maximize discrimination among the spectral reflectivities in the Non-conventional Exploitation Factors Data System (NEFDS) library. Each candidate band was weighted using a Fermi function to represent four interacting band edges for three- bands. It is found that choice of target can significantly influence the optimal choice of bands as expressed through the TCR and the Receiver Operator Characteristic curve. This study shows that whitening the image data prominently displays targets relative to backgrounds by increasing color contrast and also maintains color constancy. Three-color images are displayed by assigning red, green, blue colors directly to the whitened data set. Achieving constant colors of targets and backgrounds over time can greatly aid human viewers in the interpretation of the images and discriminate targets.

We consider a method to improve the standard RX algorithm for point target detection. In this algorithm, we weight the results from an anti-mean or anti-median filter by dividing by the standard deviation of the local environment of each suspect pixel. In this way, we lower the false alarms caused by edge points. The results of each band are then combined. Results will be shown for visible and SWIR hyperspectral imagery.

In earlier work, we have shown that starting with the first two or three principal component images, one could form a two or three-dimensional histogram and cluster all pixels on the basis of the proximity to the peaks of the histogram. Here, we discuss two major issues which arise in all classification/segmentation algorithms. The first issue concerns the desired range of segmentation levels. We explore this issue by means of plots of histogram peaks versus the scaling parameter used to map into integer bins. By taking into account the role of P_min, the minimum definition of a peak in the histogram, we demonstrate the viability of this approach. The second issue is that of devising a merit function for assessing segmentation quality. Our approach is based on statistical tests used in the Automatic Classification of Time Series (ACTS) algorithm and is shown to support and be consistent with the histogram plots.

Hyperspectral imagery provides high spectral and spatial resolution that can be used to discriminate between object and clutter occurring in subsurface remote sensing for applications such as environmental monitoring and biomedical imaging. We look at using a noncausal auto-regressive Gauss-Markov Random Field (GMRF) model to model clutter produced by a scattering media for subsurface estimation, classification, and detection problems. The GMRF model has the advantage that the clutter covariance only depends on 4 parameters regardless of the number of bands used. We review the model and parameter estimation methods using least squares and approximate maximum likelihood. Experimental and simulation model identification results are presented. Experimental data is generated by using a subsurface testbed where an object is placed in the bottom of a fish tank filled with water mixed with TiO₂ to simulate a mild to high scattering environment. We show that, for the experimental data, least square estimates produce good models for the clutter. When used in a subsurface classification problem, the GMRF model results in better broad classification with loss of some spatial structure details when compared to spectral only classification.

The acquisition of a multi-spectral data set in a single FPA integration time (snapshot) with no moving parts or scanning is possible with a Computed Tomographic Imaging Spectrometer (CTIS). CTIS instruments employ specially designed computer generated holograms (CGH) etched in an appropriate media for the wavelength band of interest as the dispersing element. The replacement of current etched CGHs with an electronically tunable liquid crystal Optical Phase Array (OPA) extends the capabilities of the CTIS by adding the ability to change its configuration while maintaining its basic motivation as a non-scanning imaging spectrometer with no moving parts. This tunability allows the dispersion, number of diffraction orders, and diffraction efficiency of the orders to be changed affecting the instrument’s spectral resolution, data cube reconstruction quality and speed. This publication presents the results of characterizing the OPA phase vs. applied voltage profile and the feedback algorithm used to program the OPA as a CTIS disperser.

An imaging spectrometer that can simultaneously obtain 3-D spatial and hyperspectral data has been developed. The Ranging-Imaging Spectrometer (RIS) is based on the Computed Tomographic Imaging Spectrometer (CTIS) developed at the Optical Science Center, and the Scannerless Laser Radar (LADAR) architecture developed at Sandia National Labs. The instrument acquires hyperspectral data in a single snapshot and spatial data in a series of snapshots. The system has 29 spectral bands, 1024 range samples, and approximately 80 x 80 spatial sampling. The RIS is discussed along with analysis of test data.

The University of Arizona's Remote Sensing Group depends heavily upon automated solar radiometers and transfer radiometers for calibration of sensors. Interference filters are essential for these devices and accuracy in determining filter transmittance characteristics is crucial. The Remote Sensing Group uses a commercially available automated spectroradiometric measurement system equipped with a dual monochromator and a filter transmittance accessory for measuring filter transmittance. Examination of the design of transmittance attachment and the detector assembly indicated the possibility of multiple back reflections between an interference filter and the detector and that higher than expected transmittance values were likely. To reduce this, a fine annealed BK7 wedge with an 8-degree deviation angle was placed in the optical path between the transmittance accessory focusing lens and the detector. The purpose of this paper is to evaluate the performance of the system with the BK7 wedge. The effect of the wedge will be negligible for an absorption filter and possibly significant for interference filters in the band-pass region. Two interference filters were analyzed via three repeats for each of the following scenarios: broadband with and without the wedge and band-pass with and without the wedge. The broadband at low spectral resolution and band-pass at high spectral resolution trials had comparable results while the greatest percent difference in transmittance occurred in the out-of-band region due to the extremely small transmittance values associated with the noise level for the instrument in general and for each interference filter specifically. For the band-pass region, the trials yielded a 0.015 to 0.062 difference in transmittance with the greatest difference occurring in the large gradient zone between the band-pass and the out-of-band region. The wedge makes a significant difference in transmittance measurements.

We present the conceptual design of an imaging Fourier transform spectrometer (IFTS) for use with SCUBA-2, the second generation, wide-field, submillimeter camera currently under development for the James Clerk Maxwell Telescope (JCMT). This system, which is planned for operation in 2006, will provide simultaneous, broadband, intermediate spectral resolution imaging across both the 850 and 450 µm bands. The spectrometer will offer variable resolution with resolving powers ranging from R ~10 to 5000. When operated at low resolution, the IFTS will provide continuum measurements, well suited to spectral index mapping of molecular clouds, as well as bright nearby galaxies. The IFTS uses a folded Mach-Zehnder configuration and novel intensity beamdividers. The preliminary design, projected telescope performance and scientific impact of the IFTS are discussed. The preliminary design, novel observing modes, projected telescope performance and scientific impact of the IFTS are discussed.

The Multispectral Thermal Imager Satellite (MTI), launched on March 12, 2000, has now surpassed its one-year mission requirement and its three-year mission goal. Primary and secondary program objectives regarding the development and evaluation of space-based multispectral and thermal imaging technology for nonproliferation treaty monitoring and other national security and civilian application have been met. Valuable lessons have also been learned, both from things that worked especially well and from shortcomings and anomalies encountered. This paper addresses lessons associated with the satellite, ground station and system operations, while companion papers address lessons associated with radiometric calibration, band-to-band registration and scientific processes and results. Things addressed in this paper that went especially well include overall satellite design, ground station design, system operations, and integration and test. Anomalies and other problems addressed herein include gyro and mass storage unit failures, battery under-voltage trips, a blown fuse, unexpected effects induced by communication link noise, ground station problems, and anomalies resulting from human error. In spite of MTI’s single-string design, the operations team has been successful in working around these problems, and the satellite continues to collect valuable mission data.

The Multispectral Thermal Imager (MTI) was designed as an imaging radiometer with absolute calibration requirements established by Department of Energy (DOE) mission goals. Particular emphasis was given to water surface temperature retrieval using two mid wave and three long wave infrared spectral bands, the fundamental requirement was a surface temperature determination of 1K at the 68% confidence level. For the ten solar reflective bands a one-sigma radiometric performance goal of 3% was established. In order to address these technical challenges a calibration facility was constructed containing newly designed sources that were calibrated at NIST. Additionally, the design of the payload and its onboard calibration system supported post launch maintenance and update of the ground calibration. The on-orbit calibration philosophy also included vicarious techniques using ocean buoys, playas and other instrumented sites; these became increasingly important subsequent to an electrical failure which disabled the onboard calibration system. This paper offers various relevant lessons learned in the eight-year process of reducing to practice the calibration capability required by the scientific mission. The discussion presented will include observations pertinent to operational and procedural issues as well as hardware experiences; the validity of some of the initial assumptions will also be explored.

The Multispectral Thermal Imager (MTI) is a technology test and demonstration satellite whose primary mission involved a finite number of technical objectives. MTI was not designed, or supported, to become a general purpose operational satellite. The role of the MTI science team is to provide a core group of system-expert scientists who perform the scientific development and technical evaluations needed to meet programmatic objectives. Another mission for the team is to develop algorithms to provide atmospheric compensation and quantitative retrieval of surface parameters to a relatively small community of MTI users. Finally, the science team responds and adjusts to unanticipated events in the life of the satellite. Broad or general lessons learned include the value of working closely with the people who perform the calibration of the data as well as those providing archived image and retrieval products. Close interaction between the Los Alamos National Laboratory (LANL) teams was very beneficial to the overall effort as well as the science effort. Secondly, as time goes on we make increasing use of gridded global atmospheric data sets which are products of global weather model data assimilation schemes. The Global Data Assimilation System information is available globally every six hours and the Rapid Update Cycle products are available over much of the North America and its coastal regions every hour. Additionally, we did not anticipate the quantity of validation data or time needed for thorough algorithm validation. Original validation plans called for a small number of intensive validation campaigns soon after launch. One or two intense validation campaigns are needed but are not sufficient to define performance over a range of conditions or for diagnosis of deviations between ground and satellite products. It took more than a year to accumulate a good set of validation data. With regard to the specific programmatic objectives, we feel that we can do a reasonable job on retrieving surface water temperatures well within the 1°C objective under good observing conditions. Before the loss of the onboard calibration system, sea surface retrievals were usually within 0.5°C. After that, the retrievals are usually within 0.8°C during the day and 0.5°C at night. Daytime atmospheric water vapor retrievals have a scatter that was anticipated: within 20%. However, there is error in using the Aerosol Robotic Network retrievals as validation data which may be due to some combination of calibration uncertainties, errors in the ground retrievals, the method of comparison, and incomplete physics. Calibration of top-of-atmosphere radiance measurements to surface reflectance has proven daunting. We are not alone here: it is a difficult problem to solve generally and the main issue is proper compensation for aerosol effects. Getting good reflectance validation data over a number of sites has proven difficult but, when assumptions are met, the algorithm usually performs quite well. Aerosol retrievals for off-nadir views seem to perform better than near-nadir views and the reason for this is under investigation. Land surface temperature retrieval and temperature-emissivity separations are difficult to perform accurately with multispectral sensors. An interactive cloud masking system was implemented for production use. Clouds are so spectrally and spatially variable that users are encouraged to carefully evaluate the delivered mask for their own needs. The same is true for the water mask. This mask is generated from a spectral index that works well for deep, clear water, but there is much variability in water spectral reflectance inland and along coasts. The value of the second-look maneuvers has not yet been fully or systematically evaluated. Early experiences indicated that the original intentions have marginal value for MTI objectives, but potentially important new ideas have been developed. Image registration (the alignment of data from different focal planes) and band-to-band registration has been a difficult problem to solve, at least for mass production of the images in a processing pipeline. The problems, and their solutions, are described in another paper.

The fifteen-channel Multispectral Thermal Imager (MTI) provides accurately calibrated satellite imagery for a variety of scientific and programmatic purposes. To be useful, the calibrated pixels from the individual detectors on the focal plane of this pushbroom sensor must be resampled to a regular grid corresponding to the observed scene on the ground. In the LEVEL1B_R_COREG product, it is required that the pixels from different spectral bands and from different sensor chip assemblies all be coregistered to the same grid. For the LEVEL1B_R_GEO product, it is further required that this grid be georeferenced to the Universal Transverse Mercator coordinate system. It is important that an accurate registration is achieved, because most of the higher level products (e.g. ground reflectance) are derived from these LEVEL1B_R products. Initially, a single direct georeferencing approach was pursued for performing the coregistration task. Although this continues to be the primary algorithm for our automated pipeline registration, we found it advantageous to pursue alternative approaches as well. This paper surveys these approaches, and offers lessons learned during the three years we have been addressing the coregistration requirements for MTI imagery at the Los Alamos National Laboratory (LANL).

The Multispectral Thermal Imager Satellite (MTI) has been used to test a sub-pixel sampling technique in an effort to obtain higher spatial frequency imagery than that of its original design. The MTI instrument is of particular interest because of its infrared detectors. In this spectral region, the detector size is traditionally the limiting factor in determining the satellite’s ground sampling distance (GSD). Additionally, many over-sampling techniques require flexible command and control of the sensor and spacecraft. The MTI sensor is well suited for this task, as it is the only imaging system on the MTI satellite bus. In this super-sampling technique, MTI is maneuvered such that the data are collected at sub-pixel intervals on the ground. The data are then processed using a deconvolution algorithm using in-scene measured point spread functions (PSF) to produce an image with synthetically-boosted GSD.

The Digital Elevation Model (DEM) extraction process traditionally uses a stereo pair of aerial photographs that are sequentially captured using an airborne metric camera. Standard DEM extraction techniques have been naturally extended to utilize satellite imagery. However, the particular characteristics of satellite imaging can cause difficulties in the DEM extraction process. The ephemeris of the spacecraft during the collects, with respect to the ground test site, is the most important factor in the elevation extraction process. When the angle of separation between the stereo images is small, the extraction process typically produces measurements with low accuracy. A large angle of separation can cause an excessive number of erroneous points in the output DEM. There is also a possibility of having occluded areas in the images when drastic topographic variation is present, making it impossible to calculate elevation in the blind spots. The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of the Multispectral Thermal Imager (MTI) allows for multiple collects of the same area to be taken from different perspectives. This functionality of MTI makes it a good candidate for the implementation of DEM extraction using multiple images for improved accuracy. This paper describes a project to evaluate this capability and the algorithms used to extract DEMs from multi-look MTI imagery.

Naval operations, including day-to-day activities as well as warfighting, depend upon the environment and are planned and executed based upon environmental knowledge. Hyperspectral sensing provides environmental information and characterization for some of these operational Naval activities. It is useful to separate these information types into "oceanographic" and "meteorological" categories as follows: (1) Coastal and shallow water operations, (2) Beach, wetland and near shore operations, (3) Self defense, and (4) Precision strike.

The Naval Research Laboratory and the Boeing Company have teamed to fly the NRL ocean Portable Hyperspectral Imager for Low Light Spectroscopy (ocean PHILLS) on board the International Space Station (ISS). This joint program is named the Hyperspectral Sensor for Global Environmental Imaging and Analysis (HyGEIA). Hyperspectral images spanning the wavelength range 400 to 1000 nm will be collected at a ground sample distance of 25 m, with 10 nm spectral binning, and 200 to 1 signal to noise over the visible wavelengths for a 5% albedo scene. These images will be used to characterize the coastal ocean and littoral zone, crops, and forest areas. The PHILLS will also image over the same wavelength range at 130 m GSD to produce similar environmental products over a larger ground area. This paper will describe the modification of PHILLS required for use on the ISS, the modeled on orbit performance, and the planned on orbit configuration.

This study focuses on Coastal land cover classification from airborne hyperspectral at two sites. Our primary study area, is a chain of barrier islands, collectively known as the Virginia Coast Reserve (VCR); the second site is located in and around Barnegat Bay, NJ. At the Barnegat Bay site, hyperspectral imagery was acquired by PHILLS during a two week campaign in late July and early August. The present work examines land-cover models for PHILLS imagery subsets acquired on August 2, 2001. At the VCR site, we have acquired an extensive time-series of PROBE2 imagery over six of the barrier islands, as well as one HyMAP scene. Multi-season models have been developed that take advantage of seasonal differences in land-cover to improve classification accuracy. Automatic classification experiments consider roughly 20-25 categories of land-cover at the two different sites. Categories include a variety of wetland plant species (brackish and freshwater), beach, dune, and upland plant species and plant communities. We also examine in detail detectability and accuracy of mapping invasive plant species such as Phragmites australis, which pose a particular challenge to natural resource managers.

We previously developed an algorithm named Tafkaa for remote sensing of ocean color from aircraft and satellite platforms. The algorithm allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser's vector radiative transfer code. During the past few years we have extended the capabilities of the code. Current modifications include the ability to account for varying solar geometry (important for very long scenes) and view geometries (important for wide fields of view). Additionally, versions of Tafkaa have been made for a variety of multi-spectral sensors, including SeaWiFS and MODIS. Here we present sample results of atmospheric corrections of data from several platforms.

To better understand the capabilities of hyperspectral imaging spectrometers, a number of organizations planned and carried out a data collection exercise at a desert site in the southwestern United States. As part of this collection, eight soil 'panels' were constructed; four filled with a coarse gravel/sand mixture and four flled with fine soil. Each set of four panels was prepared to represent two moisture and density conditions: wet versus dry and compacted versus loose. Unlike laboratory soil specimens, which use 'purified' samples, these soil flats contained more variability. They therefore better represented the 'natural' environment that would be viewed by an airborne hyperspectral imaging sensor, while still allowing an experimental study under more controlled conditions. This paper examines how well the eight soil types and conditions can be distinguished based on their VNIR/SWIR reflectance spectra derived from field measurements and from airborne hyperspectral measurements made at nearly the same time. A brief review of the phenomenology of soil reflectance spectra will be given. Based on physical attributes of the soils, some new classification approaches have been developed and were applied to the soil panels. These phenomenological methods include examining contrast in certain broadband features and, based on these, calculating various broadband spectral ratios over subsets of the VNIR/SWIR spectral region. The separability of the reflectance spectra from the eight soil panels were also analyzed by applying the Spectral Angle Mapper (SAM) hyperspectral distance metric to quantify the separations between all pairs of soil types and conditions. Finally, a neural network approach was applied to determine distinguishing features of the spectra. The phenomenological approaches, SAM analyses, and the neural network results will be compared.

The Environmental Protection Agency (EPA) Region 7 maintains an operational passive midwave/longwave airborne spectrometer system. This system provides near-real-time information on hazardous chemical releases (e.g., chemical constituents, column density and direction) for emergency personnel responding to industrial accidents. Industrial accidents range from ruptured tank cars caused by train derailments to explosions at industrial facilities. Airborne particles may be present as well, especially in accidents involving explosions and fire. This paper investigates how the presence of airborne particles can affect the identification of airborne chemical species in these situations.

The US Environmental Protection Agency is currently operating an airborne hyperspectral remote infrared spectrometer for the purpose of providing near real-time chemical data to first responders and other response agencies. This system has been designed to fulfill Agency data collection requirements for both traditional chemical emergency response and counter - terrorism activities. The platform consists of a high speed long-wave and mid-wave spectrometer and a multi-spectral infrared line scanner integrated into a mid-sized twin-engine aircraft. Through the use of onboard data processing and short haul data links, chemical information can be relayed to the end user in about ten minutes. An important component of this system is the development of the spectral reference library used to query the incoming data stream. A balance must be reached in providing a library set that is robust enough to provide useful information for the majority of accidents without the overhead of voluminous amounts of rarely used spectral library data. The end goal of the program is to generate a library set which permits a reasonable number of compounds to be automativally processed as data is streamed through the system. This paper will describe the selection technique used to develop the critical list of compounds contained in the library. This paper will likewise describe how this library is integrated into the overall system and the type of data processing and products that are produced.

We describe a systematic statistical analysis of the signal measured from three representative targets (grass-covered ground, woodline, and low angle sky) using a passive Fourier transform infrared spectroradiometer operating in the LWIR region (700-1350 wavenumbers/7.4-14 microns). Measurements were acquired under a wide variety of meteorological conditions including rain, snow, fog, and air temperatures. The instrumentation was operated in a temperature-controlled environment to minimize the impact of self-radiance on the measurements, and data were acquired at a variety of spectral resolutions. A standard deviation in radiance metric was developed and compared to the noise-equivalent spectral radiance (NESR) to quantify the statistical variability of the observed radiometric noise, and some assessments of the potential for background variance correction in detection algorithm development are drawn from the results.

The excitation-emission matrix (EEM) is the luminescence spectral emission intensity of fluorescent compounds as a function of the excitation wavelength. EEMs offer the promise of an additional degree of information for enhanced compound detection and identification. Veridian has collected pure-component EEMs of amino acids (Trp, Phe, Tyr), Bacillus globigii (bg), Bacillus thuringiensis (bt,), and selected backgrounds. Also collected were EEMs of mixtures of amino acids and of bg in solution with a few backgrounds. The EEMs of pure components and mixtures were analyzed for phenomenology and for potential methods of unmixing and identifying the constituents of EEMs having mixed components of a similar nature.

Noise estimation does not receive much attention in remote sensing society. It may be because normally noise is not large enough to impair image analysis result. Noise estimation is also very challenging due to the randomness nature of the noise (for random noise) and the difficulty of separating the noise component from the signal in each specific location. We review and propose seven different types of methods to estimate noise variance and noise covariance matrix in a remotely sensed image. In the experiment, it is demonstrated that a good noise estimate can improve the performance of an algorithm via noise whitening if this algorithm assumes white noise.

Northrop Grumman Space Technology (NGST), using internal funding, has designed, built and is testing a Long Wave Hyperspectral Imaging Spectrometer (LWHIS) that operates in the 8 to 12.5 micron band. This instrument was designed to be compatible with aircraft platforms so that flight data in this wavelength band can be used for phenomenological analysis. The instrument provides up to 256 contiguous spectral channels with 17 nm of dispersion per pixel (pixels are binned in normal operation to provide 128 spectral channels). The entrance aperture is 3.5 cm and feeds a F2/5 reflective triplet front end. The focal plane is a 256 x 256 array of 40 micron pixels which can be binned to form an 80 micron superpixel. With a fixed frame rate of 60 Hz, the instrument provides a ground sample distance of 1m at 1.1km altitude. This paper describes the physical characteristics of the design and presents the predicted performance based on NGST internal models. Design trades and test data will be presented. A more detailed look at the characterization and calibration of this instrument will be presented in a companion paper "Long Wave Hyperspectral Imaging Spectrometer -- System Characterization and Calibration."

Northrop Grumman Space Technology (NGST) has developed and tested a Long-wave Hyperspectral Imaging Spectrometer (LWHIS) that operates in the 8 to 12.5 micron band. An overview of the system design has been described elsewhere. This paper describes the system characterization and radiometric calibration of this instrument using NGST’s Long-wave Hyperspectral Test Facility which uses a 1375K globar source assembly, a monochromator, a collimator and a fine pointing mirror to provide image quality and FPA alignment data. Image quality characterization results presented here include measurement of the instrument’s Modulation Transfer Function (MTF), spatial co-registration of spectral channels (spectral smile), cross-track spectral error (spatial smile), and spectral calibration. Radiometric calibration results for laboratory targets are also presented.

A long-wave infrared hyperspectral sensor using an interferometer and an uncooled microbolometer array camera was built and tested. The sensor showed signal to noise ratios near 200 for ambient temperature scenes with 33 wavenumber resolution at a frame rate of 50 Hz. Straightforward performance improvements in optics and reported detector performances suggest signal to noise ratios near 1000 at 10 wavenumber resolution should be achievable.

CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) is a hyperspectral imager that will be launched on the MRO (Mars Reconnaissance Orbiter) in August 2005. The MRO will circle Mars in a polar orbit at a nominal altitude of 325 km. The CRISM spectral range spans the ultraviolet (UV) to the mid-wave infrared (MWIR), 400 nm to 4050 nm. The instrument utilizes a Ritchey-Chretien telescope with a 2.06º field of view (FOV) to focus light on the entrance slit of a dual spectrometer. Within the spectrometer light is split by a dichroic into VNIR (visible-near infrared) (λ ≤ 1.05 μm) and IR (infrared) (λ ≥ 1.05 μm) beams. Each beam is directed into a separate modified Offner spectrometer that focuses a spectrally dispersed image of the slit onto a two dimensional focal plane (FP). The IR FP is a 640 x 480 HgCdTe area array; the VNIR FP is a 640 x 480 silicon photodiode area array. The spectral image is contiguously sampled with a 6.55 nm spectral spacing and an instantaneous field of view of 60 μradians. The orbital motion of the MRO pushbroom scans the spectrometer slit across the Martian surface, allowing the planet to be mapped in 558 spectral bands. There are four major mapping modes: A quick initial multi-spectral mapping of a major portion of the Martian surface in 59 selected spectral bands at a spatial resolution of 600 μradians (10:1 binning); an extended multi-spectral mapping of the entire Martian surface in 59 selected spectral bands at a spatial resolution of 300 μradians (5:1 binning); a high resolution Target Mode, performing hyperspectral mapping of selected targets of interest at full spatial and spectral resolution; and an atmospheric Emission Phase Function (EPF) mode for atmospheric study and correction at full spectral resolution at a spatial resolution of 300 μradians (5:1 binning). The instrument is gimbaled to allow scanning over ±60° for the EPF and Target modes. The scanning also permits orbital motion compensation, enabling longer integration times and consequently higher signal-to-noise ratios for selected areas on the Martian surface in Target Mode.

In this paper we present some laboratory measurements obtained by a new imaging interferometer. This instrument has been derived from the so called “stationary interferometers,” which do not employ any moving part to optically scan the instrument field-of-view. The device acquires the image of an object superimposed to a fixed (stationary) pattern of autocorrelation functions of the energy coming from each pixel. The interference pattern, constituted by a system of vertical fringes, is scanned by moving the observed target with respect to the imaging device. In order to calibrate the optical-path-difference axis of the raw interferograms, we have executed a set of measurements employing a He-Ne laser source spread by a pair of planar diffusers. The dependence of the optical-path-difference values on the source spectral content has been addressed performing a set of measurements after filtering a 600W halogen lamp with interference filters of 10nm bandwidth. We have described the procedure of pre-processing of the acquired data to retrieve the spectrum of at-sensor radiance (dark signal subtraction, spectral instrument response compensation, effects of vignetting and Fourier transform algorithm). Some hints are given about the use of this instrument from airborne platforms for remote sensing of the Earth.

Many spectral signature detection algorithms depend on numerically inverting covariance matrices. Hyperspectral data rarely span the full band space because of factors such as sensor noise, numerical round-off, sparse sampling, and band correlation inherent in the data or introduced by data processing. Processing the full order of the covariance matrix without regard to its useful rank leads to reduced detection performance. It was previously shown that the performance of inverse-covariance based detection algorithms can be improved by regularizing the covariance matrix inversion through extension of an optimally chosen eigenvalue. The extension method provides a robust way to optimize signal to clutter ratio (SCR) on data collected with a detector of uniform gain. The method of trusted eigenvalue extension has now been applied to data collected with a sensor with multiple gain regions. Multiple gain regions are used on wide spectral range sensors such as HYDICE and complicate the inversion of the covariance matrix over the full range of spectral bands. Further optimization of the trusted eigenvalue is presented and compared against traditional regularization methods. Since the extension method is particularly intended for sparsely sampled data with high dimensionality, a comparison is presented between the extension method and band coaddition.

An investigation of methods for class mean and covariance initialization of a stochastic mixing model for hyperspectral imagery is described along with other relevant issues concerning algorithm convergence such as updating of the class priors, constraining the mixture classes and the number of fraction levels and endmember classes. The various refinements of the iterative algorithm are presented and tested on synthetically-generated test data as well as real reflective hyperspectral imagery, and recommendations are made concerning how the stochastic mixing model can be best implemented. The results show that the refined stochastic mixng model is a robust approach for unmixing hyperspectral imagery with different levels of complexity.

Synthetic imagery has traditionally been used to support sensor design by enabling design engineers to pre-evaluate image products during the design and development stages. Increasingly exploitation analysts are looking to synthetic imagery as a way to develop and test exploitation algorithms before image data are available from new sensors. Even when sensors are available, synthetic imagery can significantly aid in algorithm development by providing a wide range of “ground truthed” images with varying illumination, atmospheric, viewing and scene conditions. One limitation of synthetic data is that the background variability is often too bland. It does not exhibit the spatial and spectral variability present in real data. In this work, four fundamentally different texture modeling algorithms will first be implemented as necessary into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model environment. Two of the models to be tested are variants of a statistical Z-Score selection model, while the remaining two involve a texture synthesis and a spectral end-member fractional abundance map approach, respectively. A detailed comparative performance analysis of each model will then be carried out on several texturally significant regions of the resultant synthetic hyperspectral imagery. The quantitative assessment of each model will utilize a set of three peformance metrics that have been derived from spatial Gray Level Co-Occurrence Matrix (GLCM) analysis, hyperspectral Signal-to-Clutter Ratio (SCR) measures, and a new concept termed the Spectral Co-Occurrence Matrix (SCM) metric which permits the simultaneous measurement of spatial and spectral texture. Previous research efforts on the validation and performance analysis of texture characterization models have been largely qualitative in nature based on conducting visual inspections of synthetic textures in order to judge the degree of similarity to the original sample texture imagery. The quantitative measures used in this study will in combination attempt to determine which texture characterization models best capture the correct statistical and radiometric attributes of the corresponding real image textures in both the spatial and spectral domains. The motivation for this work is to refine our understanding of the complexities of texture phenomena so that an optimal texture characterization model that can accurately account for these complexities can be eventually implemented into a synthetic image generation (SIG) model. Further, conclusions will be drawn regarding which of the candidate texture models are able to achieve realistic levels of spatial and spectral clutter, thereby permitting more effective and robust testing of hyperspectral algorithms in synthetic imagery.

The Optical Real-time Adaptive Spectral Identification System (ORASIS) continues to be developed by the Naval Research Laboratory. Recently, new methods to compress hyperspectral data using ORASIS in combination with wavelets have been demonstrated. Variations on this approach continue to be pursued and will be discussed. In addition, a new method to perform target identification based on Spectral Angle Mapper (SAM) has been demonstrated. When projected into a reduced dimension space, angles between spectra are not conserved. Proper determination of the dimensions and coordinates of the subspace can result in maximizing the angles between a spectrum of interest and the general background. This approach can be used for target identification where the "target" can be a rare spectrum or something found in the general background. Recent improvements to these methods, as well as new applications of the software, are discussed.

The Orthogonal Subspace Projection (OSP) and Constrained Energy Minimization (CEM) have been used in hyperpsectral target detection and classification. A target-constrained interference-minimized filter (TCIMF) was recently proposed to extend the CEM to improve signal detectability to annihilating undesired target signal sources as the way carried out in the OSP. In this paper, we revisit the TCIMF from a signal processing viewpoint where signals can be characterized by three types of information sources, desired target sources and undesired target sources, both of which are provided a priori, and interferers which are unknown interfering sources. By virtue of such signal decomposition, we chan show that the TCIMF is actually a generalization of the OSP and CEM. In particular, we investigate assumptions made for the OSP and CEM in terms of these three types of signal sources and exploit insights into their filter design. As will be shown in this paper, the OSP and the CEM perform the same tasks by operating different levels of information and both can be viewed as special cases of the TCIMF.

Fully constrained linear spectral mixture analysis (FCLSMA) has been used for material quantification in remotely sensed imagery. In order to implement FCLSMA, two constraints are imposed on abundance fractions, referred to as Abundance Sum-to-one Constraint (ASC) and Abundance Nonnegativity Constraint (ANC). While the ASC is linear equality constraint, the ANC is a linear inequality constraint. A direct approach to imposing the ASC and ANC has been recently investigated and is called fully constrained least-squares (FCLS) method. Since there is no analytical solution resulting from the ANC, a modified fully constrained least-squares method (MFCLS) which replaces the ANC with an Absolute Abundance Sum-to-one Constraint (AASC) was proposed to convert a set of inequality constraints to a quality constraint. The results produced by these two approaches have been shown to be very close. In this paper, we take an oopposite approach to the MFCLS method, called least-squares with linear inequality constraints (LSLIC) method which also solves FCLSMA, but replaces the ASC with two linear inequalities. The proposed LSLIC transforms the FCLSMA to a linear distance programming problem which can be solved easily by a numerical algorithm. In order to demonstrate its utility in solving FCLSMA, the LSLIC method is compared to the FCLS and MFCLS methods. The experimental results show that these three methods perform very similarly with only subtle differences resulting from their problem formations.

The University of Hawaii AHI LWIR hyperspectral sensor has been in active use for several years. Since previous publications the sensor characteristics have evolved, and new applications have been encountered. This paper reviews the current status of the sensor and its characteristics, reviews a gas detection experiment conducted using natural sulfur dioxide emitted from a Hawaiian volcano, and test images from a hyperspectral polarization upgrade.

The University of Hawaii’s Airborne Hyperspectral Imager (AHI) consists of a long-wave infrared pushbroom hyperspectral imager and a boresighted 3-color visible high resolution CCD linescan camera. A new data system was added to the AHI in a recent upgrade of the sensor, resulting in the ability to collect data at full resolution in 256 spectral channels. This upgrade motivated the design of a new calibration procedure that removes image distortion and bad pixels from the produced imagery. The approach used is a novel method using a runtime-calculated transform. This transform describes the means of converting the distorted AHI focal plane into a corrected “virtual” AHI focal plane. The transform is formulated using several spatial-statistical assumptions as to the way information varies on the focal plane, and is based on geostatistical interpolation techniques. This transform removes the distortion present in the AHI imager and delivers high quality imagery.

We present results of field evaluations of computed tomography imaging spectrometer (CTIS) systems, using an adaptive optics (AO) compensated telescope, for applications in astronomical multispectral imaging and imaging spectroscopy in both visible (0.4 - 0.7 μm) and near infrared (1.2 - 2.1 μm) bands. We develop and analyze a convolution model for this hyperspectral imaging method which includes a convolution representation of the effects of the AO system. We use expectation maximization, with additional prior constraints derived from the physics of the instrumental, statistical and atmospheric effects inherent in such observations, to extract details of features in the hyperspectral images. Data for this study were obtained using the Advanced Electro-Optics System (AEOS) -- a facility of the Air Force Maui Space Surviellance System.

This paper describes the initial flight experience of the Compact High Resolution Imaging Spectrometer (CHRIS) developed at Sira Electro-Optics Ltd. The imaging spectrometer is flying on PROBA, a small agile satellite, which was launched in October 2001. The main purpose of the instrument is to provide images of land areas. The platform provides pointing in both across-track and along-track directions, for target acquisition and multi-angle observations, particularly for measurement of the Bi-directional Reflectance Distribution Function (BRDF) properties of selected targets. The platform also provides pitch motion compensation during imaging in order to increase the integration time of the instrument, increasing the number of spectral bands that can be read and enhancing radiometric resolution. The instrument covers a spectral range from 400 nm to 1050 nm, at ≤11 nm resolution. The spatial sampling interval at perigee is approximately 17 m. In this mode it is possible to read out 19 spectral bands. The locations and widths of the spectral bands are programmable. Selectable on-chip integration can increase the number of bands to 63 for a spatial sampling interval of 34 m. The swath width imaged is 13 km at perigee.