Optimal interpretation of remote sensing imagery requires characterizing the atmospheric composition between a sensor and the area it is observing. Timely estimates of atmospheric temperature, water vapor, and other constituents from the ground to the edge of the space environment are not always readily available. In those cases, we must supplement our knowledge of the atmosphere’s composition to fill in any gaps in knowledge and empirical models of the atmosphere are useful tools for this purpose. The Standardized Atmosphere Generator (SAG) was constructed is one such empirical. It has been designed to allow all the major known, systematic variability in the atmosphere and may be used to generate atmospheric profile from the ground to 300 km consistent with user-specified temporal, geophysical, and geographical information Output provides reasonable estimates for temperature, pressure, and densities of atmospheric constituents and can be directly incorporated into radiative transfer forward models or retrieval algorithms. SAG draws upon a number of existing empirical atmospheric models and ensures consistency of output between them. It can be used either as a stand-alone interactive program or scripted for batch execution and assist in determining atmospheric attenuation, refraction, scattering, chemical kinetic temperature profiles, and a host of other naturally occurring processes. Here, we will discuss the capabilities and performance of the SAG model for a variety of applications including its interactive and batch processing use. We will also demonstrate the physical realism of SAG through a small number of relevant use cases.
In this paper, we present examples of aerosol and Cirrus cloud altitude profiles over Hanoi, Vietnam, measured
with the ground LIDAR setup of the Institute of Physics. Comparisons are made to LIDAR data collected by the Calipso
satellite of the NASA A-Train during its orbits over the Hanoi area. The height distributions for both surface aerosols
and Cirrus clouds derived from ground and satellite observations are generally consistent, with distributions between
2km-3km, and 8km-15km respectively for aerosols and Cirrus clouds. Cirrus cloud locations inferred from an analysis of
limb spectral radiances obtained by the SCIAMACHY satellite are also consistent with the LIDAR data.
Singular value decomposition (SVD) and principal component analysis enjoy a broad range of applications, including,
rank estimation, noise reduction, classification and compression. The resulting singular vectors form orthogonal basis
sets for subspace projection techniques. The procedures are applicable to general data matrices. Spectral matrices
belong to a special class known as non-negative matrices. A key property of non-negative matrices is that their
columns/rows form non-negative cones, with any non-negative linear combination of the columns/rows belonging to the
cone. This special property has been implicitly used in popular rank estimation techniques know as virtual dimension
(VD) and hyperspectral signal identification by minimum error (HySime). Data sets of spectra reside in non-negative
orthants. The subspace spanned by a SVD of a set of spectra includes all orthants. However SVD projections can be
constrained to the non-negative orthants. In this paper two types of singular vector projection constraints are identified,
one that confines the projection to lie within the cone formed by the spectral data set, and a second that only restricts
projections to the non-negative orthant. The former is referred to here as the inner constraint set, the latter the outer
constraint set. The outer constraint set forms a broader cone since it includes projections outside the cone formed by the
data array. The two cones form boundaries for the cones formed by non-negative matrix factorizations (NNF).
Ambiguities in the NNF lead to a variety of possible sets of left and right non-negative vectors and their cones. The
paper presents the constraint set approach and illustrates it with applications to spectral classification.
This paper presents results that demonstrate the auroral modeling capabilities of the Air Force Research Laboratory
(AFRL) SAMM2 (SHARC And MODTRAN® Merged 2) radiance code. A scene generation capability is obtained by
coupling SAMM2 with a recently developed Clutter Region Atmosphere and Scene Module (CRASMO), which
provides an approach for rapid generation of time sequences and images of radiance clutter. Modeled results will be
compared to data collected by the Midcourse Space Experiment (MSX)1 in the IR and UV-visible spectral regions during
an auroral event on November 10, 1996.
The paper is organized as follows. We first present a brief history of the AFRL SHARC/SAMM codes, leading up to the
current version, SAMM2 v.2. The SAMM2 UV-visible auroral kinetic model will then be described, followed by a
comparison of modeled results to the MSX data.
Subspace methods for hyperspectral imagery enable detection and identification of targets under unknown
environmental conditions (i.e., atmospheric, illumination, surface temperature, etc.) by specifying a subspace of possible
target spectral signatures (and, optionally, a background subspace) and identifying closely fitting spectra in the image.
The subspaces, defined from a set of exemplar spectra, are compactly expanded in singular value decomposition basis
vectors or, less commonly, endmember basis spectra, linear combinations of which are used to fit the image data. In the
present study we compared detection performance in the thermal infrared using several different constrained and
unconstrained basis set expansions of low-dimensional subspaces, including a method based on the Sequential
Maximum Angle Convex Cone (SMACC) endmember algorithm. Constrained expansions were found to provide a
modest improvement in algorithm robustness in our test cases.
KEYWORDS: Matrices, Long wavelength infrared, Data modeling, Imaging spectroscopy, Signal to noise ratio, Infrared radiation, Infrared sensors, Sensors, Data processing, Principal component analysis
A new non-negative factorization method has been developed. The method is based on the concept of non-negative
rank (NNR). Bounds for the NNR of certain non-negative matrices are determined relative to the
rank of the matrix, with the lower bound being equal to the rank. The method requires that the data matrix be
non-negative and have a large first singular value. Unlike other non-negative factorization methods, the
approach does not assume or require that the factors be linearly independent and no assumption of statistical
independence is required. The rank of the matrix provides the number of linearly independent components
present in the data while the non-negative rank provides the number of non-negative independent components
present in the data. The method is described and illustrated in application to hyperspectral data sets.
We investigated the contributions of the hydroxyl (OH) airglow to the illumination of resident space objects.
During nighttime, in a moonless sky, the airglow is the largest contributor to the sky brightness in the visible
(vis), the near-infrared (NIR) and short-wave infrared (SWIR) spectral region. The dominant contributors to
the airglow are vibrationally excited hydroxyl radicals, OH(ν). The radicals are formed in vibrational states
up to υ=9 by the reaction of hydrogen atoms with ozone. The strong emissions, known as Meinel emissions,
are sequences with σν= 1-6. Emissions with υ = 3, 4, 5 and 6 occur in the visible and NIR between .4 and 1.0
µm. From 1.0 to 2.5 µm there are very strong emissions from the δν= 2 sequences. The σν= 1 emissions
extend into the thermal infrared to 4.5 μm. In this work, we considered four band passes, a vis-NIR band
pass, two SABER band passes centered at 1.6 and 2.0 μm, respectively, and a broad band pass around 2.7
µm. SAMM2 was utilized to compute spectra and line of sight radiances. We used line of sight (LOS)
radiances to compute the irradiance on a space object that was taken as a flat plate with a Lambertian surface
reflectance. Profiles of irradiance versus orientation were calculated. The OH airglow will illuminate a facet
even if it is pointing somewhat upward. However, the irradiance in the 2.7 μm band pass comes almost
entirely from the atmosphere in the low altitude and the earth emission.
A new correlated-k algorithm has recently been incorporated into SAMM-2, the Air Force Research Laboratory
background radiance and transmission code. SAMM-2 incorporates all of the major components necessary for
background scene generation at all altitudes: atmospheric characterization, solar irradiance, molecular chemical kinetics
and molecular spectroscopic data. The underlying physical models are applicable for both low-altitude local
thermodynamic equilibrium (LTE) conditions as well as high-altitude non-LTE (NLTE) conditions. Comprehensive
coverage in the .4 to 40 micron (250 to 25,000 wavenumber) wavelength region for arbitrary lines-of-sight (LOS) in the
0 to 300 kilometer altitude regime is provided. A novel 1 cm-1 resolution correlated-k algorithm has been developed in
order to provide the orders-of-magnitude increase in computational efficiency when compared to the existing SAMM-2
line-by-line (LBL) algorithm and applicable to both LTE and NLTE atmospheric conditions. The SAMM-2 correlated-k
algorithm processes molecular lines at runtime by reading line center information from the HITRAN 2000 database and
computing statistical cumulative probability distributions within a spectral interval under the presumption of a Voigt line
shape profile. This algorithm is useful for treating atmospheric phenomena at all altitudes requiring a spectrally
monochromatic treatment of the atmospheric transmission and/or radiance, including multiple scattering or atmospheric structure.
KEYWORDS: Reflectivity, Data modeling, Atmospheric modeling, Monte Carlo methods, Correlation function, Scene simulation, 3D image processing, Sensors, Hyperspectral simulation, Atmospheric particles
A method for extracting statistics from hyperspectral data and generating synthetic scenes suitable for scene generation models is presented. Regions composed of a general surface type with a small intrinsic variation, such as a forest or crop field, are selected. The spectra are decomposed using a basis set derived from spectra present in the scene and the abundances of the basis members in each pixel spectrum found. Statistics such as the abundance means, covariances and channel variances are extracted. The scenes are synthesized using a coloring transform with the abundance covariance matrix. The pixel-to-pixel spatial correlations are modeled by an autoregressive moving average texture generation technique. Synthetic reflectance cubes are constructed using the generated abundance maps, the basis set and the channel variances. Enhancements include removing any pattern from the scene and reducing the skewness. This technique is designed to work on atmospherically-compensated data in any spectral region, including the visible-shortwave infrared HYDICE and AVIRIS data presented here. Methods to evaluate the performance of this approach for generating scene textures include comparing the statistics of the synthetic surfaces and the original data, using a signal-to-clutter ratio metric, and inserting sub-pixel spectral signatures into scenes for detection using spectral matched filters.
A multiple simplex endmember extraction method has been developed. Unlike convex methods that rely on a single simplex, the number of endmembers is not restricted by the number of linearly independent spectral channels. The endmembers are identified as the extreme points in the data set. The algorithm for finding the endmembers can
simultaneously find endmember abundance maps. Multispectral and hyperspectral scenes can be complex and contain many materials under a variety of illumination and environmental conditions, but individual pixels typically contain only a few materials in a small subset of the illumination and environmental conditions which exist in the scene. This forms the physical basis for the approach that restricts the number of endmembers that combine to model a single pixel. No restriction is placed on the total number of endmembers, however. The algorithm for finding the endmembers and their abundances maps is sequential. Extreme points are identified based on the angle they make with the existing set. The point making the maximum angle with the existing set is chosen as the next endmember to add to enlarge the endmember set. The maximum number of endmembers that are allowed to be in a subset model for individual pixels is controlled by an input parameter. The subset selection algorithm is sequential and takes place simultaneously with the overall endmember extraction. The algorithm updates the abundances of previous endmembers and ensures that the abundances of previous and current endmembers remain positive or zero. The method offers advantages in multispectral data sets where the limited number of channels impairs material un-mixing by standard techniques. A description of the method is presented herein and applied to real and synthetic hyperspectral and multispectral data sets.
A new endmember extraction method has been developed that is based on a convex cone model for representing vector data. The endmembers are selected directly from the data set. The algorithm for finding the endmembers is sequential: the convex cone model starts with a single endmember and increases incrementally in dimension. Abundance maps are simultaneously generated and updated at each step. A new endmember is identified based on the angle it makes with the existing cone. The data vector making the maximum angle with the existing cone is chosen as the next endmember to add to enlarge the endmember set. The algorithm updates the abundances of previous endmembers and ensures that the abundances of previous and current endmembers remain positive or zero. The algorithm terminates when all of the data vectors are within the convex cone, to some tolerance. The method offers advantages for hyperspectral data sets where high correlation among channels and pixels can impair un-mixing by standard techniques. The method can also be applied as a band-selection tool, finding end-images that are unique and forming a convex cone for modeling the remaining hyperspectral channels. The method is described and applied to hyperspectral data sets.
A new end-member analysis method based on convex cones has been developed. The method finds extreme points in a convex set. Unlike convex methods that rely on a simplex, the number of end-members is not restricted by the number of spectral channels. The algorithm simultaneously finds fractional abundance maps. The fractional abundances are the fractions of the total spectrally integrated radiance of a pixel that are contributed by the end-members. A physical model of the hyper-spectral or multi-spectral scene is obtained by combining subsets of the end-members into bundles of spectra for each scene material. The bundle spectra represent the spectral variability of the material in the scene induced by illumination, shadowing, weathering and other environmental effects. The method offers advantages in multi-spectral data sets where the limited number of channels impairs material un-mixing by standard techniques. The method can also be applied to compress hyper-spectral data. The fractional abundance matrices are sparse and offer an additional compression capability over standard matrix factorization techniques. A description of the method and applications to real and synthetic hyper-spectral and multi-spectral data sets will be presented.
KEYWORDS: Atmospheric modeling, Reflectivity, Target detection, Monte Carlo methods, Data modeling, Scene simulation, Atmospheric particles, Detection and tracking algorithms, Algorithm development, Visible radiation
A method for the extraction of spectral and spatial scene statistics from hyperspectral data is discussed. The method is designed to work on atmospherically compensated data in any spectral region, although this paper will report on visible scene statistics derived from atmospherically compensated AVIRIS data. Our approach is based on a physical description where the scene is composed of materials that in turn are described by a set of spectral endmembers. The spatial statistics of individual scene materials have more stationary behavior than the statistics for the whole scene. For this reason we have formulated our approach around statistics that are determined from the fractional abundance images obtained from the spectral un-mixing of the scene. These quantities are used to construct a high spatial resolution reflectance or emissivity/temperature surface using a fast autoregressive texture generation tool. The spectral complexity of the synthetic surfaces have been evaluated by inserting objects for detection and calculating ROC curves. Preliminary results indicate that synthetic scenes with realistic levels of spectral clutter can be generated using spectral and spatial statistics determined from endmember fractional abundance maps. This work is motivated by the need for realistic hyperspectral scene generation capabilities to test future hyperspectral sensor concepts.
Model-based atmospheric correction of multi-spectral and hyperspectral imagery (MSI/HSI) typically involves searching through a look-up table (LUT) of potential atmospheric representations for a best fit, based on some fit criterion. These representations are generated using a radiation transport model such as MODTRAN. The parameter space covered by the LUT is defined to cover the likely atmospheric conditions encountered by the sensor that affect observed radiance over the spectral region covered by the sensor. For instance, aerosols play an important role in the visible through SWIR (450-2500 nm) but a minor role in the thermal IR, where water column content and atmospheric temperature are critical. We investigate the sampling and representation of the atmospheric parameter space in the thermal IR as it effects retrieval of the atmosphere. Using the SMACC convex projection technique we evaluate selection of significant basis members from a broadly-based LUT. We apply SMACC selected endmembers to solve for an arbitrary atmosphere.
The atmospheric correction of thermal infrared (TIR) imagery involves the combined tasks of separation of atmospheric transmittance, downwelling flux and upwelling radiance from the surface material spectral emissivity and temperature. The problem is ill posed and is thus hampered by spectral ambiguity among several possible feasible combinations of atmospheric temperature, constituent profiles, and surface material emissivities and temperatures. For many materials, their reflectance spectra in the Vis-SWIR provide a means of identification or at least classification into generic material types, vegetation, soil, etc. If Vis-SWIR data can be registered to TIR data or collected simultaneously as in sensors like the MASTER sensor, then the additional information on material type can be utilized to help lower the ambiguities in the TIR data. If the Vis-SWIR and TIR are collected simultaneously the water column amounts obtained form the atmospheric correction of the Vis-SWIR can also be utilized in reducing the ambiguity in the atmospheric quantities. The TIR atmospheric correction involves expansions in atmospheric and material emissivity basis sets. The method can be applied to hyperspectral and ultraspectral data, however it is particularly useful for multispectral TIR, where spectral smoothness techniques cannot be readily applied. The algorithm is described, and the approach applied to a MASTER sensor data set.
KEYWORDS: Atmospheric modeling, Atmospheric particles, Reflectivity, Sensors, Monte Carlo methods, Data modeling, Scene simulation, Correlation function, Image processing, Software
A method for the extraction of spectral and spatial scene statistics from hyperspectral data is discussed. The method is designed to work on atmospherically compensated data in the visible/SWIR or the Thermal IR (TIR). The statistics are determined from the fractional abundance images obtained from spectral un-mixing of the scene. The statistical quantities that are extracted include endmember abundance means, variances, and correlation lengths. These quantities are used to construct a high spatial resolution reflectance or emissivity/temperature surface using a fast autoregressive texture generation tool. The spectral complexity of the synthetic surfaces have been evaluated by inserting objects for detection and calculating ROC curves. Preliminary results indicate that synthetic scenes with realistic levels of spectral clutter can be generated using spectral and spatial statistics determined from endmember fractional abundance maps. This work is motivated by the need for realistic hyperspectral scene generation capabilities to test future hyperspectral sensor concepts.
An extensive database on spatial structure in the infrared radiance of the middle and upper atmosphere has been collected by the Mid-Course Space Experiment (MSX). The observed radiance contains spatial structure down to the scale of hundreds of meters. This spatial structure results from local fluctuations in the temperature and densities of the radiating states of the emitting molecular species as well as fluctuations in radiation transport from the emitting regions to the observer. A portion of this database has been analyzed to obtain statistical parameters characterizing stochastic spatial structure in the observed radiance. Using simple models, the observed statistics have been shown to agree with prior observations and theoretical models of stochastic spatial structure generated by gravity waves for special viewing geometries. The SHARC model has been extended to predict the statistics of stochastic fluctuations in infrared radiance from the statistics characterizing temperature fluctuations in the middle and upper atmosphere for arbitrary viewing geometries. SHARC model predictions have been compared with MSX data and shown to be in generally good agreement. Additional work is in progress to account for the statistics characterizing small spatial scale fluctuations.
Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sum and by the sky. Some specific algorithms and applications are illustrated using HYperspectral Digital Imagery Collection Experiment (HYDICE) data.
KEYWORDS: Sensors, Signal processing, Imaging spectroscopy, Reflectivity, Image sensors, Remote sensing, Signal to noise ratio, Data compression, Remote sensing system design, Optical resolution
A method of optimizing the selection of spectral channels in a spectral-spatial remote sensor has been developed that is applicable to the design of multispectral, hyperspectral and ultra spectral resolution sensors. The approach is based on an end member analysis technique that has been refined to select the most information dense channels. The algorithm operates sequentially and at any step in the sequence, the channel selected is the most independent form all previously selected channels. After the channel selection process, highly correlated channels, which are contiguous to those selected, can be merged to form bands. This process increases the signal to noise for the new broader spectral bands. The resulting bands, potentially of unequal width and spacing, collect the most uncorrelated spectral information present in the data. The band selection provides a physical interpretation of the data and has applications in spectral feature selection and data compression.
Radiation transport modulates the spatial frequencies of atmospheric structures, acting as a low pass filter, which causes the power spectra of the accumulated radiance to have different power spectral slopes than the underlying atmospheric structure. Additional effects arise because of the non-stationarity of the atmosphere. The SHARC atmospheric radiance code is used to model both non- stationarity of the atmosphere. The SHARC atmospheric radiance code is used to model both equilibrium and non- equilibrium radiance and radiance fluctuation statistics. It predicts two dimensions. Radiance spatial covariance functions and power spectral densities, PSDs. Radiance power spectral slopes for paths through isotropic Kolmogorov turbulence are predicted to vary from -5/3 to -8/3 depending on the length of the path through the turbulence. The input gravity wave 3D covariances and PSDs of atmospheric temperature are consistent with current gravity wave theory, having vertical and horizontal power spectral indices of -3 and -5/3, respectively. Altitude profiles of variances and correlation lengths account of the non-stationary of the gravity wave structure in the atmosphere. The radiance covariance and PSD power spectral slopes differ from the atmospheric gravity wave temperature model values of -3 and -5/3. These modulations depend on LOS orientations, and scale lengths of the sampled altitudes along the LOS.
A technique has been developed to estimate bounds on the spectra of major constituents of multispectral images. The bounds are two distinct sets of spectra, one in which the spectra are maximally independent from one another and another set in which the spectra a re minimally independent. Both sets and their corresponding estimated abundance maps satisfy feasibility constraints for both spectral elements and fractional abundances. The actual spectra will have an independence measure between the minimal and maximum sets. An approach to mapping the feasibility region for all intermediate independence measures is described. In general, for a given level of independence here is an infinity of rotation axes about which small rotations of the spectra leads to another feasible set. In our approach, the selected rotation axes is the one which takes the maximally independent basis into the minimally independent basis. The effects of noise and low levels of additional components are expected to have a larger effect on altering the spectra than the modifications due to small arbitrary rotations of feasible spectra. The technique is illustrated by application to a computer generated multispectral data array.
The Quick Image Display (QUID) model accurately computes and displays radiance images at animation rates while the target undergoes unrestricted flight or motion. Animation rates are obtained without sacrificing radiometric accuracy by using three important innovations. First, QUID has been implemented using the OpenGL graphics library which utilizes available graphics hardware to perform the computationally intensive hidden surface calculations. Second, the thermal emission and reflectance calculations are factorized into angular and wavelength dependent terms for computational efficiency. Third, QUID uses OpenGL supported texture mapping to simulate pseudo curved surface reflectance. The size of the glint texture is controlled by paint/surface properties and the surface normals at the facet's vertices. QUID generates IR radiance maps, in-band and spectral signatures for high level of detail targets with thousands of facets. Model features are illustrated with radiance and radiance contrast images of aircraft.
Atmospheric infrared radiance fluctuations result from fluctuations in the density of atmospheric species, individual molecular state populations, and kinetic temperatures and pressures along the sensor line of sight (LOS). The SHARC-4 program models the atmospheric background radiance fluctuations. It predicts a two dimensional radiance spatial covariance function from the underlying 3D atmospheric structures. The radiance statistics are non-stationary and are dependent on bandpass, sensor location and field of view (FOV). In the upper atmosphere non-equilibrium effects are important. Fluctuations in kinetic temperature can result in correlated or anti-correlated fluctuations in vibrational state temperatures. The model accounts for these effects and predicts spatial covariance functions for molecular state number densities and vibrational temperatures. SHARC predicts the non-equilibrium dependence of molecular state number density fluctuations on kinetic temperature and density fluctuations, and calculates mean LOS radiances and radiance derivatives. The modeling capabilities are illustrated with sample predictions of MSX like experiments with MSX sensor bandpasses, sensor locations and FOV. The model can be applied for all altitudes and arbitrary sensor FOV including nadir and limb viewing.
Simulation of infrared radiance fluctuations in the atmosphere depends on detailed descriptions of fluctuations in atmospheric species number densities, vibrational state populations, and the kinetic temperatures along the sensor line-of-sight. The relationship between kinetic and vibrational temperature fluctuations depends on the subtle interplay between changes in the total number densities, changes in the temperature-dependent kinetic rates, and the relative contribution of the radiative relaxation. The model developed in this paper predicts the two- dimensional radiance covariance function for nonequilibrium effect conditions. The radiance statistics are non-stationary and are explicitly bandpass and sensor FOV dependent. The SHARC model is used to calculate mean LOS radiance values and radiance derivatives which are necessary to determine the radiance statistics. Inputs to the model include the statistical parameters of a non-stationary atmospheric temperature fluctuation model and an atmospheric profile. The radiance statistics are used in a simple model for synthesizing images. The model has been applied to calculate the radiance structure for the OH((Delta) v equals 1) SWIR band and the CO2((nu) 3) MWIR band under nighttime conditions.
This paper describes the development of a new version of the SHARC code, SHARC-3, which includes the ability to simulate changing atmospheric conditions along the line-of-sight (LOS) paths being calculated. SHARC has been developed by the U.S. Air Force for the rapid and accurate calculation of upper atmospheric IR radiance and transmittance spectra with a resolution of better than 1 cm-1 in the 2 to 40 micrometers (250 to 5,000 cm-1) wavelength region for arbitrary LOSs in the 50 - 300 km altitude regime. SHARC accounts for the production, loss, and energy transfer processes among the molecular vibrational states important to this spectral region. Auroral production and excitation of CO2, NO, and NO+ are included in addition to quiescent atmospheric processes. Calculated vibrational temperatures are found to be similar to results from other non-LTE codes, and SHARC's equivalent-width spectral algorithm provides very good agreement with much more time-consuming `exact' line-by-line methods. Calculations and data comparisons illustrating the features of SHARC-3 are presented.
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