This PDF file contains the front matter associated with SPIE Proceedings Volume 7086, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Microspectrometers, miniature spectrometers, portable spectrometers, or Fiber Optic Spectrometers are some of the names typically given to the class small spectrometers that are derived from simple, fixed optics, and low cost detector arrays. The author will use these terms interchangeably. This class of instrument has been available for over 18 years, gaining industry acceptance with each year. From a very basic optical platform to sophisticated instrumentation for scientific investigation and process control, this class of instrument has evolved substantially since its introduction to the market. For instance it is now possible to cover the range from 200 - 2,500 nm utilizing only two channels of spectrometers with either synchronous or asynchronous channel control. On board processing and memory have enabled the instruments to become fully automated, stand alone sensors communicating with their environment via analog, digital, USB2 and even wireless protocols. New detectors have entered the market enabling solutions "tuned" to the demands of specific applications.

Measurements of complete polarization and spectral content across a broad wavelength range of a scene are used in various fields including astronomy, remote sensing, and target detection. Most current methods to acquire spectral and polarimetric information need moving parts or modulation processes which lead to significant complexity or reduce sampling resolution. Here we present a novel snapshot imaging spectropolarimeter based on anisotropic diffraction gratings known as polarization gratings (PGs). Using multiple PGs and waveplates, we can acquire both spectrally dispersed and highly polarized diffractions of a scene on a single focal plane array, simultaneously. PGs uniquely produce only three diffracted orders (0 and ±1), polarization independent zerothorder, polarization sensitive first-orders that depend linearly with the Stokes parameters, and easily fabricated as polymer films suitable for visible to infrared wavelength operation. The most significant advantage of our spectropolarimeter over other snapshot imaging systems is its capability to provide simultaneous acquisition of both spectral and polarization information at a higher resolution and in a simpler and more compact way. Here we report our preliminary data and discuss the cogent design of our imaging spectropolarimeter.

A computed tomographic imaging spectrometer (CTIS) is an instrument which can simultaneously obtain image spatial and spectral information without moving parts in a single focal plane array integration time. When this instrument is combined with a channeled spectropolarimeter, the instrument can also obtain complete Stokes polarization information at each resolution element. The combined instrument, called a computed tomographic imaging channeled spectropolarimeter (CTICS), has been developed in the visible wavelength region. This paper summarizes the CTICS design and results obtained from data acquired during field testing of the CTICS instrument.

The Environmental Mapping and Analysis Program (EnMAP) is a German space based hyperspectral mission planned for launch in 2012. The hyperspectral instrument covers the wavelength range from 420nm to 2450nm using a dual spectrometer layout. Both f/3 spectrometers employ a prism disperser for maximum throughput and are linked to the common foreoptics by a micromechanical field splitter. Together with custom designed silicon and MCT-based detector arrays this sensor design exhibits a peak system SNR of 1000 at 495nm and of more than 300 at 2200nm. Stable and precise in orbit performance is ensured by a multi loop thermal control system and a system calibration which relies on onboard sources as well as a full aperture diffuser.

Preliminary results are presented for an ultra compact long-wave infrared slit spectrometer based on the Dyson concentric design. The spectrometer has been integrated in a dewar environment with a quantum well infrared photodetecor (QWIP), concave electron beam fabricated diffraction grating and ultra precision slit. The entire system is cooled to cryogenic temperatures to maximize signal to noise ratio performance, hence eliminating thermal signal from transmissive elements and internal stray light. All of this is done while maintaining QWIP thermal control. A general description is given of the spectrometer, alignment technique and predicated performance. The spectrometer has been designed for optimal performance with respect to smile and keystone distortion. A spectral calibration is performed with NIST traceable targets. A 2-point non-uniformity correction is performed with a precision blackbody source to provide radiometric accuracy. Preliminary laboratory results show excellent agreement with modeled noise equivalent delta temperature and detector linearity over a broad temperature range.

The automated detection of chemical spectral signatures using a passive infrared Fourier Transform Infrared (FT-IR) Spectrometer mounted in an aircraft is a difficult challenge due to the small total infrared energy contribution of a particular chemical species compared to the background signature. The detection of spectral signatures is complicated by the fact that a large, widely varying infrared background is present that is coupled with the presence of a number of chemical interferents in the atmosphere. This paper describes a mathematical technique that has been successfully demonstrated to automatically detect specific chemical species in an automated processing environment. The data analysis methodology has been demonstrated to be effective using data of low spectral resolution at low aircraft altitudes. An overview of the implementation and basic concepts of the approach are presented.

Plant stresses, in particular fungal diseases, show a high variability in spatial and temporal dimension with respect to their impact on the host. Recent "Precision Agriculture"-techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stressdetection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is an accurate sensor-based detection of stress symptoms. The present study focuses on a remotely sensed detection of the fungal disease powdery mildew (Blumeria graminis) in wheat, Europe's main crop. In a field experiment, the potential of hyperspectral data for an early detection of stress symptoms was tested. A sophisticated endmember selection procedure was used and, additionally, a linear spectral mixture model was applied to a pixel spectrum with known characteristics, in order to derive an endmember representing 100% powdery mildew-infected wheat. Regression analyses of matched fraction estimates of this endmember and in-field-observed powdery mildew severities showed promising results (r=0.82 and r²=0.67).

A fast and precise sensor-based identification of pathogen infestations in wheat stands is essential for the implementation of site-specific fungicide applications. Several works have shown possibilities and limitations for the detection of plant stress using spectral sensor data. Hyperspectral data provide the opportunity to collect spectral reflectance in contiguous bands over a broad range of the electromagnetic spectrum. Individual phenomena like the light absorption of leaf pigments can be examined in detail. The precise knowledge of stress-dependent shifting in certain spectral wavelengths provides great advantages in detecting fungal infections. This study focuses on band selection techniques for hyperspectral data to identify relevant and redundant information in spectra regarding a detection of plant stress caused by pathogens. In a laboratory experiment, five 1 sqm boxes with wheat were multitemporarily measured by a ASD Fieldspec® 3 FR spectroradiometer. Two stands were inoculated with Blumeria graminis - the pathogen causing powdery mildew - and one stand was used to simulate the effect of water deficiency. Two stands were kept healthy as control stands. Daily measurements of the spectral reflectance were taken over a 14-day period. Three ASD Pro Lamps were used to illuminate the plots with constant light. By applying band selection techniques, the three types of different wheat vitality could be accurately differentiated at certain stages. Hyperspectral data can provide precise information about pathogen infestations. The reduction of the spectral dimension of sensor data by means of band selection procedures is an appropriate method to speed up the data supply for precision agriculture.

N-finder algorithm (N-FINDR) is probably one of most popular and widely used algorithms used for endmember extraction. Three major obstacles need to be overcome in its practical implementation. One is that the number of endmembers must be known a priori. A second one is the use of random initial endmembers to initialize N-FINDR, which results in inconsistent final results of extracted endmembers. A third one is its very expensive computational cost caused by an exhaustive search. While the first two issues can be resolved by a recently developed concept, virtual dimensionality (VD) and custom-designed initialization algorithms respectively, the third issue seems to remain challenging. This paper addresses the latter issue by re-designing N-FINDR which can generate one endmember at a time sequentially in a successive fashion to ease computational complexity. Such resulting algorithm is called SeQuential N-FINDR (SQ N-FINDR) as opposed to the original N-FINDR referred to as SiMultaneous N-FINDR (SM N-FINDR) which generates all endmembers simultaneously at once. Two variants of SQ N-FINDR can be further derived to reduce computational complexity. Interestingly, experimental results show that SQ N-FINDR can perform as well as SM-N-FINDR if initial endmembers are appropriately selected.

This paper introduces a new signature coding which is designed based on the well-known Block Truncation Coding (BTC). It comprises of bit-maps of the signature blocks generated by different threshold criteria. Two new BTC-based algorithms are developed for signature coding, to be called Block Truncation Signature Coding (BTSC) and 2-level BTSC (2BTSC). In order to compare the developed BTC based algorithms with current binary signature coding schemes such as Spectral Program Analysis Manager (SPAM) developed by Mazer et al. and Spectral Feature-based Binary Coding (SFBC) by Qian et al., three different thresholding functions, local block mean, local block gradient, local block correlation are derived to improve the BTSC performance where the combined bit-maps generated by these thresholds can provide better spectral signature characterization. Experimental results reveal that the new BTC-based signature coding performs more effectively in characterizing spectral variations than currently available binary signature coding methods.

A traditional linear-mixing model with a structured background used in the hyperspectral imaging literature often assumes normality (Gaussianity) of the error term. This assumption is often questioned. In previous research, we show that the normal (Gaussian) distribution gives only a very crude approximation to the actual error term distribution. In this paper, we use a broader class of distributions called exponential power (or error) distributions. We investigate suitability of those distributions using a specific example of an AVIRIS hyperspectral image. We demonstrate that the exponential power distributions provide a satisfactory description of the marginal error term distributions for the AVIRIS hyperspectral image used in this paper.

Infrared spectrometer data from the space-based downward looking NASA Atmospheric Infrared Sounder (AIRS) and from the ground-based upward looking Atmospherically Emitted Radiance Interferometer (AERI) are used in this study. Spatially and temporally concurrent cloud free data from these spectrometers are correlated, analyzed, and compared to MODTRAN®5 simulated data. The effects of optical depth, water vapor, ozone, carbon dioxide, and methane on infrared remote sensing applications are characterized.

The novel mid-wave infrared (MWIR) retrieval method described in this paper is based on an idea derived from retrieving accurate sea surface temperatures for the Multispectral Thermal Imager (MTI) which uses the "Same Temperature Criterion", i.e., that retrieved skin temperatures in all bands must be the same. We apply it to simulated MODIS datasets. In this paper we present retrieval errors for this method for a range of atmospheric conditions and skin surface variations. The STC retrieval method is applied to actual, de-striped MODIS data, and we show that it is capable of retrieving subtle high spatial resolution (1 km) water vapor variations over ocean surfaces. The variance of the retrieved surface temperature is a useful criterion for finding sub-pixel area hotspots (e.g. forest fires, lava flows, gas flares, furnaces, boilers, etc) and distinguish them from warm water bodies at night.

The optical properties of a surface may change significantly in response to contaminants from the environment and/or human activity. We utilize a first principles, physics-based radiometric ray tracing software package to evaluate the spectral polarimetric bi-directional reflectance distribution function (BRDF) of the virgin and contaminated surfaces. In the absence of contaminants, we find the simulated reflectance properties of randomly rough Gaussian surfaces to be well represented by micro-facet based polarimetric BRDF models. However the addition of contaminants introduces phenomenology that falls outside the basic assumptions of the micro-facet BRDF models. We present results of BRDF simulations of painted surfaces with liquid and solid contaminants.

Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state is one important application. Analysis of the surface emissivity as a means to classify ground properties and usage is another one. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on the Fourier Transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320x256 pixels at 0.35mrad spatial resolution over the 8-12 μm spectral range at spectral resolutions of up to 0.25cm^-1. The FIRST has been used in several field campaigns, including the demonstration of standoff chemical agent detection [http://dx.doi.org/10.1117/12.795119.1]. More recently, an airborne system integrating the FIRST has been developed to provide airborne hyperspectral measurement capabilities. The airborne system and its capabilities are presented in this paper. The FIRST sensor modularity enables operation in various configurations such as tripod-mounted and airborne. In the airborne configuration, the FIRST can be operated in push-broom mode, or in staring mode with image motion compensation. This paper focuses on the airborne operation of the FIRST sensor.

We have developed and fabricated a Tl₃AsSe₃ (TAS) crystal based acousto-optic tunable filter (AOTF) for operation between the 8 to 12.0 μm wavelength regions. We have demonstrated peak efficiency greater than 60% with a 10.6 μm source and 2 watts of RF input power. This high efficiency should enable high resolution and large throuput for AOTF based imaging and spectroscopic systems.

In this paper, a dual-channel spectral imaging system with agile spectral band access and spectral bandwidth tuning capability is presented. A diffractive grating is used as the spectral dispersion element for the dual-channel spectral imaging system. A 4-f spectral filtering channel using an adjustable slit is set up at the first diffraction order of the grating to realize coarse spectral band selection. An acousto-optic tunable filter selectively filters the spectrum of the non-dispersed zero order to realize fine spectral imaging. The spectral zooming function is achieved without increasing spectral frame number facilitating real-time spectral imaging operation. Feasibility of the spectral imaging has been demonstrated through preliminary experiments. Minimum 6 nm spectral resolution and 1.2° field of view have been achieved. The real-time spectral imaging capable of wide spectral band operation without loosing desired fine spectral capability is particularly useful for a variety of defense, medical, and environmental monitoring applications.

Hyperspectral images are used for anomaly detection; the improvement over broadband imagery is due to the available spectral information, converting a two-dimensional image into a datacube. This paper deals with subpixel point target detection. The RX algorithm provides a statistical metric for how different an examined pixel is from the background in the data cube. It employs an inverse covariance matrix to estimate and limit the effect of the noise, which is normally estimated from all the pixels in the data cube. Since the background is non-stationary, an improvement in the detection performance can be achieved by segmentation. The first objective of this paper is to quantify in the algorithm the effect of using covariance matrices which are derived from the segments or the more local environments in which the pixel can be found. In addition, practically, pixels may be erroneously assigned to a specific segment though they are influenced by neighboring areas. We will examine different methods of choosing the "pure" pixels of each segment, and the influence of these methods on the probability of detection results.

This work investigates target detection using simulated hyperspectral imagery captured from highly oblique angles. This paper seeks to determine which domain, radiance or reflectance, is more appropriate for the off-nadir case. An oblique atmospheric compensation technique based on the empirical line method (ELM) is presented and used to compensate the simulated data used in this study. The resulting reflectance cubes are subjected to a variety of standard target detection processes. A forward modeling technique that is appropriate for use on oblique hyperspectral data is also presented. This forward modeling process allows for standard target detection techniques to be applied in the radiance domain. Results obtained from the radiance and reflectance domains are comparable. Under ideal circumstances, however, the radiance domain results are slightly better than the results observed in the reflectance domain. These somewhat favorable results for the radiance domain, considered with the practicality and potential operational applicability of the forward modeling technique presented, suggest that the radiance domain is an attractive option for oblique hyperspectral target detection.

One of the most challenging issues in unsupervised target analysis is how to obtain unknown target knowledge directly from the data to be processed. This issue has never arisen in supervised target analysis where the target knowledge is either assumed to be known or provided by a priori. However, with recent advent of sensor technology many unknown and subtle signal sources can be uncovered and revealed by high spectral imaging spectrometers such as hyperspectral imaging sensors. The knowledge of these signal sources generally cannot be obtained by assumed or prior knowledge. Under this circumstance supervised target analysis may not be realistic or applicable. This paper addresses the issue of how to generate such knowledge for data analysis and further develops unsupervised target finding algorithms for target analysis. In order to demonstrate the utility of the developed unsupervised target finding algorithms, experiments are conducted for applications in unsupervised linear spectral unmixing.

Spectral Fingerprint Identification (SFI) attempts to incorporate feature finding, text matching, and data fusion techniques for fast whole cube material identification. In operation, the SFI algorithm translates spectral data into a feature space where fast text matching between all pixels in a data cube and a preprocessed SFI spectral library can be performed. Data fusion of the resulting feature matches creates a listing of materials likely contained in a data cube at both the whole pixel and subpixel level. The Spectral Fingerprint Identification methodology was implemented in a prototype Opticks plug-in capable of both standalone and Windows based cluster processing.

The ARTEMIS hyperspectral sensor will be the first spaceborne hyperspectral sensor with an on-board real-time processing capability. The ARTEMIS real-time processor utilizes both anomaly and material detection algorithms to locate materials of potential interest. To satisfy the real-time processing timelines, the collected data must be reduced from hundreds of bands to around 64 bins, where a bin can be a single band or the average of a set of bands. A signature optimization study was conducted to compare various binning algorithms through the analysis of both the detection characteristics and the discrimination performance before and after spectral binning.

Automated image endmember extraction from hyperspectral imagery is a challenge and a critical step in spectral mixture analysis (SMA). Over the past years, great efforts were made and a large number of algorithms have been proposed to address this issue. Iterative error analysis (IEA) is one of the well-known existing endmember extraction methods. IEA identifies pixel spectra as a number of image endmembers by an iterative process. In each of the iterations, a fully constrained (abundance nonnegativity and abundance sum-to-one constraints) spectral unmixing based on previously identified endmembers is performed to model all image pixels. The pixel spectrum with the largest residual error is then selected as a new image endmember. This paper proposes an updated version of IEA by making improvements on three aspects of the method. First, fully constrained spectral unmixing is replaced by a weakly constrained (abundance nonnegativity and abundance sum-less-or-equal-to-one constraints) alternative. This is necessary due to the fact that only a subset of endmembers exhibit in a hyperspectral image have been extracted up to an intermediate iteration and the abundance sum-to-one constraint is invalid at the moment. Second, the search strategy for achieving an optimal set of image endmembers is changed from sequential forward selection (SFS) to sequential forward floating selection (SFFS) to reduce the so-called "nesting effect" in resultant set of endmembers. Third, a pixel spectrum is identified as a new image endmember depending on both its spectral extremity in the feature hyperspace of a dataset and its capacity to characterize other mixed pixels. This is achieved by evaluating a set of extracted endmembers using a criterion function, which is consisted of the mean and standard deviation of residual error image. Preliminary comparison between the image endmembers extracted using improved and original IEA are conducted based on an airborne visible infrared imaging spectrometer (AVIRIS) dataset acquired over Cuprite mining district, Nevada, USA.