We describe a new algorithm, QUAC-IR (QUick Atmospheric Correction in the InfraRed), for automated, fast, atmospheric correction of LWIR (Long Wavelength InfraRed) hyperspectral imagery (HSI) and multi-spectral imagery (MSI) in the ~7-14 mm spectral region. QUAC-IR is an in-scene based algorithm, similar to the widely used ISAC (In- Scene Atmospheric Correction) algorithm. It improves upon the ISAC approach in several key ways, including providing absolute, versus relative, sensor-to-ground transmittances and radiances, as well as an estimate of the atmospheric downwelling sky radiance. The latter is important for retrieving emissivity from a reflective (i.e., non-blackbody) pixel. The key aspect of QUAC-IR is that it explicitly searches for blackbody pixels using an efficient approach involving a small number of spectral channels in which the atmospheric radiative transfer is dominated by the water continuum. This allows for fast and simplified Beer's Law (i.e., exponential) scaling of the path transmittance and radiance based on a compact library of pre-computed reference values. We apply QUAC-IR to well-calibrated data from the SEABASS1 and MAKO2 HSI sensors. The results are compared to those from a first-principles physics-based atmospheric code, FLAASH-IR.
Extremely thick haze caused by air pollution is observed in many satellite images of the earth, and in particular over eastern China. Standard image display software typically provides satisfactory visualization of the ground through automated or user-driven scaling to enhance contrast; however, it does not perform well with these highly polluted scenes, where the haze is spatially non-uniform. Furthermore, estimation of surface reflectance using standard atmospheric correction software is highly problematic under these conditions due to very low visible transmission of the haze coupled with lack of knowledge of its optical properties, which may not conform to the haze or aerosol models in the software. In this paper we show that a version of the empirical Quick Atmospheric Correction (QUAC) algorithm, adapted for spatially dependent scattering, produces visually satisfying imagery of the entire ground in multispectral satellite scenes containing thick haze, and that the output reflectance spectra appear to be realistic enough for performing basic surface classification. The QUAC algorithm is applicable to multispectral and hyperspectral imagery with any number of wavelength bands, including true color (RGB) imagery, and does not require radiometrically calibrated data.
Within the last few years, several commercial long-wave infrared (LWIR) hyperspectral imaging (HSI) systems have been developed for remote sensing of the ground from aircraft. While much less expensive and more practical to operate than sensors such as SEBASS and MAKO, which have been developed primarily for research and Government use, the commercial systems have poorer signal-to-noise and/or spectral resolution. We investigate the utility of three commercial systems—the Telops Hyper-Cam, SPECIM AisaOWL, and ITRES TASI-600—for quantitative retrieval of surface temperature and emissivity spectra. Atmospheric retrieval, correction and temperature-emissivity separation are performed on example data from these sensors using FLAASH-IR, a first-principles algorithm that incorporates radiation transport calculations and atmosphere models from MODTRAN. The results from the commercial sensors are noisy compared with SEBASS but otherwise appear to be reasonable. Applying a noise suppression algorithm to the radiance data yields better temperature retrievals and much cleaner emissivity spectra, with minimal loss of information, and should benefit scene classification applications.
KEYWORDS: Hyperspectral imaging, Detection and tracking algorithms, Algorithm development, Light sources and illumination, Data processing, Silver, Short wave infrared radiation, Gold, Sensors, Sun
Multispectral and hyperspectral imaging can facilitate vehicle tracking across a series of images by gathering spectral information that distinguishes the vehicle of interest from confusers. Developing effective algorithms for utilizing this information requires an understanding of the sources and nature of both the common and unique components in vehicle spectra, as well as the variations associated with lighting, view angle, and part of the vehicle being observed. In this study, focusing on the VNIR-SWIR spectral region, we analyze hyperspectral data from a recent field experiment at the Rochester Institute of Technology. We describe the spectra of painted vehicle surfaces in general terms, and demonstrate effective classification of automobiles based on spectra from upward facing surfaces (the roof, hood or trunk) using a method that combines the Support Vector Machine with data pre-conditioning.
Hyperspectral imaging (HSI) sensors have the ability to detect and identify objects within a scene based on the distinct attributes of their surface spectral signatures. Many targets of interest, such as vehicles, represent a complex arrangement of specular (non-Lambertian) materials with curved and flat surfaces oriented at varying view factors. This complexity, combined with possible changing atmospheric/illumination conditions and viewing geometries, can produce significant variations in the observed signatures from measurement to measurement, making detection and/or reacquisition challenging. This paper focuses on the characterization of visible-near infrared-short wave infrared (VNIR-SWIR) spectra for detection, identification and tracking of vehicles. Signature variations are predicted using a novel image simulation tool to calculate spectral images of complex 3D objects from a spectral material description such as the modified Beard-Maxwell BRDF model, a wireframe shape model, and a directional model of the illumination. We compare the simulations with recent VNIR-SWIR hyperspectral imagery of vehicles and panels collected at the Rochester Institute of Technology during an Autumn 2015 measurement campaign. Variations in both the simulated and measured spectra arise mainly from differences in the relative glint contribution. Implications of these variations on vehicle detection and identification are briefly discussed.
Algorithms for retrieval of surface reflectance, emissivity or temperature from a spectral image almost always assume uniform illumination across the scene and horizontal surfaces with Lambertian reflectance. When these algorithms are used to process real 3-D scenes, the retrieved “apparent” values contain the strong, spatially dependent variations in illumination as well as surface bidirectional reflectance distribution function (BRDF) effects. This is especially problematic with horizontal or near-horizontal viewing, where many observed surfaces are vertical, and where horizontal surfaces can show strong specularity. The goals of this study are to characterize long-wavelength infrared (LWIR) signature variability in a HSI 3-D scene and develop practical methods for estimating the true surface values. We take advantage of synthetic near-horizontal imagery generated with the high-fidelity MultiService Electro-optic Signature (MuSES) model, and compare retrievals of temperature and directional-hemispherical reflectance using standard sky downwelling illumination and MuSES-based non-uniform environmental illumination.
Surface solar radiation forecasting permits to predict photovoltaic plant production for a massive and safe integration of solar energy into the electric network. For short-term forecasts (intra-day), methods using images from meteorological geostationary satellites are more suitable than numerical weather prediction models. Forecast schemes consist in assessing cloud motion vectors and in extrapolating cloud patterns from a given satellite image in order to predict cloud cover state above a PV plant. Atmospheric motion vectors retrieval techniques have been studied for several decades in order to improve weather forecasts. However, solar energy forecasting requires the extraction of cloud motion vectors on a finer spatial- and time-resolution than those provided for weather forecast applications. Even if motion vector retrieval is a wide research field in image processing related topics, only block-matching techniques are operationally used for solar energy forecasts via satellite images. In this paper, we propose two motion vectors extraction methods originating from video compression techniques (correlation phase and optical flow methods). We implemented them on a 6-day dataset of Meteosat-10 satellite diurnal images. We proceeded to cloud pattern extrapolation and compared predicted cloud maps against actual ones at different time horizons from 15 minutes to 4 hours ahead. Forecast scores were compared to the state-of-the-art (block matching) method. Correlation phase methods do not outperform block-matching but their computation time is about 25 times shorter. Optical flow based method outperforms all the methods with a satisfactory time computing.
Processing long-wave infrared (LWIR) hyperspectral imagery to surface emissivity or reflectance units via atmospheric
compensation and temperature-emissivity separation (TES) affords the opportunity to remotely classify and identify
solid materials with minimal interference from atmospheric effects. This paper describes an automated atmospheric
compensation and TES method, called FLAASH®-IR (Fast Line-of-sight Atmospheric Analysis of Spectral Hypecubes--
Infrared), and its application to ground-to-ground imagery taken with the Telops Inc. Hyper-Cam interferometric
hyperspectral imager. The results demonstrate that clean, quantitative surface spectra can be obtained, even with highly
reflective (low emissivity) objects such as bare metal and in the presence of some illumination from the surroundings. In
particular, the atmospheric compensation process suppresses the spectral features due to atmospheric water vapor and
ozone, which are especially prominent in reflected sky radiance.
A second-generation long-wave hyperspectral imager based on micro-electro-mechanical systems (MEMS) technology is in development. Spectral and spatial encoding using a MEMS digital micro-mirror device enables fast, multiplexed data acquisition with arbitrary spectral response functions. The imager may be programmed to acquire spectrally selective contrast imagery, replacing more time-consuming hyperspectral data collection. A single-element detector collects encoded data and embedded real-time hardware generates imagery. An internal scanning mechanism enables rapid retrieval of full hyperspectral imagery. The resulting rugged, low-cost sensor will provide chemically specific imagery for applications in gaseous and surface contaminant detection, surveillance, remote sensing, and process control.
Striping effects, i.e., artifacts that vary systematically with the image column or row, may arise in hyperspectral or multispectral imagery from a variety of sources. One potential source of striping is a physical effect inherent in the measurement, such as a variation in viewing geometry or illumination across the image. More common sources are instrumental artifacts, such as a variation in spectral resolution, wavelength calibration or radiometric calibration, which can result from imperfect corrections for spectral “smile” or detector array nonuniformity. This paper describes a general method of suppressing striping effects in spectral imagery by referencing the image to a spectrally lowdimensional model. The destriping transform for a given column or row is taken to be affine, i.e., specified by a gain and offset. The image cube model is derived from a subset of spectral bands or principal components thereof. The general approach is effective for all types of striping, including broad or narrow, sharp or graduated, and is applicable to radiance data at all optical wavelengths and to reflectance data in the solar (visible through short-wave infrared) wavelength region. Some specific implementations are described, including a method for suppressing effects of viewing angle variation in VNIR-SWIR imagery.
Field test results are presented for a prototype long-wave adaptive imager that provides both hyperspectral imagery and contrast imagery based on the direct application of hyperspectral detection algorithms in hardware. Programmable spatial light modulators are used to provide both spectral and spatial resolution using a single element detector. Programmable spectral and spatial detection filters can be used to superimpose any possible analog spectral detection filter on the image. In this work, we demonstrate three modes of operation, including hyperspectral imagery, and one and two-dimensional imagery using a generalized matched filter for detection of a specific target gas within the scene.
KEYWORDS: Clouds, Monte Carlo methods, 3D modeling, Atmospheric modeling, Sun, Image fusion, Sensors, Point spread functions, Atmospheric sensing, Transmittance
A calculation method has been developed for rapidly synthesizing radiometrically accurate ultraviolet through longwavelengthinfrared spectral imagery of the Earth for arbitrary locations and cloud fields. The method combines cloudfree surface reflectance imagery with cloud radiance images calculated from a first-principles 3-D radiation transport model. The MCScene Monte Carlo code [1-4] is used to build a cloud image library; a data fusion method is incorporated to speed convergence. The surface and cloud images are combined with an upper atmospheric description with the aid of solar and thermal radiation transport equations that account for atmospheric inhomogeneity. The method enables a wide variety of sensor and sun locations, cloud fields, and surfaces to be combined on-the-fly, and provides hyperspectral wavelength resolution with minimal computational effort. The simulations agree very well with much more time-consuming direct Monte Carlo calculations of the same scene.
The quick atmospheric correction (QUAC) code performs atmospheric correction on multi- and hyperspectral imagery spanning all or part of the visible and near infrared-short wave infrared spectral range, ∼ 400−2500 nm. It utilizes an in-scene approach, requiring only approximate specification of sensor band locations (i.e., central wavelengths) and their radiometric calibration; no additional metadata is required. Because QUAC does not involve first principles radiative-transfer calculations, it is significantly faster than physics-based methods; however, it is also more approximate. We present a detailed description of the QUAC algorithm, highlighting recent accuracy improvements. Example results for several multi-and hyperspectral data sets are presented, and comparisons are made to more rigorous correction approaches.
Remotely sensed spectral imagery of the earth's surface can be used to fullest advantage when the influence of the atmosphere has been removed and the measurements are reduced to units of reflectance. Here, we provide a comprehensive summary of the latest version of the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes atmospheric correction algorithm. We also report some new code improvements for speed and accuracy. These include the re-working of the original algorithm in C-language code parallelized with message passing interface and containing a new radiative transfer look-up table option, which replaces executions of the MODTRAN® model. With computation times now as low as ~10 s per image per computer processor, automated, real-time, on-board atmospheric correction of hyper- and multi-spectral imagery is within reach.
Land and ocean data product generation from visible-through-shortwave-infrared multispectral and hyperspectral
imagery requires atmospheric correction or compensation, that is, the removal of atmospheric absorption and scattering
effects that contaminate the measured spectra. We have recently developed a prototype software system for automated,
low-latency, high-accuracy atmospheric correction based on a C++-language version of the Spectral Sciences, Inc.
FLAASH™ code. In this system, pre-calculated look-up tables replace on-the-fly MODTRAN® radiative transfer
calculations, while the portable C++ code enables parallel processing on multicore/multiprocessor computer systems.
The initial software has been installed on the Sensor Web at NASA Goddard Space Flight Center, where it is currently
atmospherically correcting new data from the EO-1 Hyperion and ALI sensors. Computation time is around 10 s per
data cube per processor. Further development will be conducted to implement the new atmospheric correction software
on board the upcoming HyspIRI mission's Intelligent Payload Module, where it would generate data products in nearreal
time for Direct Broadcast to the ground. The rapid turn-around of data products made possible by this software
would benefit a broad range of applications in areas of emergency response, environmental monitoring and national
defense.
Clouds and cloud fields introduce important backscattering, obscuration, shadowing and radiative trapping effects in visible-NIR(near-infrared)-SWIR(short-wavelength infrared) hyperspectral imagery of the ground, especially in off-nadir (slant) viewing geometries where cloud thickness effects reduce the cloud-free line of sight (CFLOS). An investigation of these effects was conducted using monochromatic, multispectral and hyperspectral scene simulations performed with the Spectral Sciences, Inc. MCScene Monte Carlo code. Cloud fields were obtained from the Cloud Scene Simulation Model (CSSM) of Cianciolo and Raffensberger. The simulations took advantage of a data-fusion-based noise-removal method that enabled a dramatic reduction in computation time. Illumination levels at the sunlit ground showed enhancements of up to ~50% due to cloud scattering. Illumination in the cloud shadows was 20% of the full solar illumination or greater, with cloud optical depths of up to 10. Most of this illumination arises from solar scattering off the cloud tops and sides; however, a significant part can be ascribed to radiative trapping between the ground and the clouds, as represented by a local atmospheric spherical albedo. A simulation of a hyperspectral scene with cloud shadows was found to reproduce shadowing effects found in real data. Deeper shadowing is observed with increasing wavelength and in water-band regions, consistent with a previous analysis of cloud shadows in real imagery. The MCScene calculations also predict shadow enhancements of column water vapor retrievals from atmospheric correction/compensation codes, also in accord with field observations. CFLOS fractions were calculated as a function of off-nadir viewing angle and were found to be very accurately represented by a semi-empirical analytical function of both angle and cloud cover.
KEYWORDS: Sensors, Digital micromirror devices, Imaging systems, Optical filters, Electronic filtering, Long wavelength infrared, Micromirrors, Interference (communication), Detection and tracking algorithms, Signal to noise ratio
Dispersive transform spectral imagers with both one- and two-dimensional spatial coverage have been demonstrated and
characterized for applications in remote sensing, target classification and process monitoring. Programmable spatial
light modulators make it possible to adjust spectral, temporal and spatial resolution in real time, as well as implement
detection algorithms directly in the digitally controlled sensor hardware. Operating parameters can be optimized in real
time, in order to capture changing background and target evolution. Preliminary results are presented for short wave,
mid-wave, and long-wave infrared sensors that demonstrate the spatial and spectral versatility and rapid adaptability of
this new sensor technology.
The QUAC (Quick Atmospheric Correction) algorithm for in-scene-based atmospheric correction of VIS-SWIR
(VISible-Short Wave InfraRed) Multi- and Hyperspectral Imagery (MSI and HSI) is reviewed and applied to
radiometrically uncalibrated data. Quite good agreement was previously demonstrated for the retrieved pixel spectral
reflectances between QUAC and the physics-based atmospheric correction code FLAASH (Fast Line-of-sight
Atmospheric Analysis of Spectral Hypercubes) for a variety of HSI and MSI data cubes. In these code-to-code
comparisons, all the data cubes were obtained with well-calibrated sensors. However, many sensors operate in an
uncalibrated manner, precluding the use of physics-based codes to retrieve surface reflectance. The ability to retrieve
absolute spectral reflectances from such sensors would significantly increase the utility of their data. We apply QUAC
to calibrated and uncalibrated versions of the same Landsat MSI data cube, and demonstrate nearly identical retrieved
spectral reflectances for the two data sets.
Sensor jitter introduces non-white noise fluctuations in imagery of cluttered scenes. These fluctuations are a major
source of interference in the detection of weak time-dependent signals, which may be associated with a subject's
appearance, motion, or brightness modulation. Due to the presence of sensor pattern noise and uncertainty in the
scene's subpixel spatial structure, standard frame-to-frame registration methods have limited ability to model and
remove these fluctuations. A simple temporal whitening approach, applicable to a wide variety of imaging systems, is
found to be highly effective for suppressing subpixel jitter effects, leading to dramatic (up to several orders of
magnitude) improvement in signal detection ability.
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.
The ability to rapidly calculate at-sensor radiance over a large number of lines of sight (LOSs) is critical for
hyperspectral and multispectral scene simulations and look-up table generation, both of which are
increasingly used for sensor design, performance evaluation, data analysis, and software and systems
evaluations. We have demonstrated a new radiation transport (RT) capability that combines an efficient
multiple-LOS (MLOS) multiple scattering (MS) algorithm with a broad-bandpass correlated-k methodology
called kURT-MS, where kURT stands for correlated-k-based Ultra-fast Radiative Transfer. The MLOS
capability is based on DISORT and exploits the existing MODTRAN-DISORT interface. kURT-MS is a new
sensor-specific fast radiative transfer formalism for UV-visible to LWIR wavelengths that is derived from
MODTRAN's correlated-k parameters. Scattering parameters, blackbody and solar functions are cast as a
few sensor-specific and bandpass-specific k-dependent source terms for radiance computations. Preliminary
transmittance results are within 2% of MODTRAN with a two-orders-of-magnitude computational savings.
Preliminary radiance computations in the visible spectrum are within a few percent of MODTRAN results,
but with orders of magnitude speed up over comparable MODTRAN runs. This new RT capability
(embodied in two software packages: kURT-MS and MODTRAN-kURT) has potential applications for
remote sensing applications such as hyperspectral scene simulation and look-up table generation for
atmospheric compensation analysis as well as target acquisition algorithms for near earth scenarios.
Compared to nadir viewing, off-nadir viewing of the ground from a high-altitude platform provides opportunities to increase area coverage and to reduce revisit times, although at the expense of spatial resolution. In this study, the ability to atmospherically compensate off-nadir hyperspectral imagery taken from a space platform was evaluated for a worst-case viewing geometry, using EO-1 Hyperion data collected with an off-nadir angle of 63° at the sensor, corresponding to six air masses along the line of sight. Reasonable reflectance spectra were obtained using both
first-principles (FLAASH) and empirical (QUAC)
atmospheric-compensation methods. Some refinements to FLAASH that enable visibility retrievals with highly off-nadir imagery, and also improve accuracy in nadir viewing, were developed and are described.
The ability to rapidly calculate at-sensor radiance over a large number of lines of sight (LOSs) is critical for scene
simulations, which are increasingly used for sensor design, performance evaluation, and data analysis. We have recently
demonstrated a new radiation transport (RT) capability that combines an efficient multiple-LOS multiple scattering
algorithm with a broad-bandpass correlated-k methodology called kURT-MS. The multiple-LOS capability is based on
DISORT and exploits the existing MODTRAN-DISORT interface. kURT-MS is a new sensor-specific correlated-k (c-k)
ultra-fast radiative transfer formalism for UV-visible to LWIR wavelengths that is derived from MODTRAN's
correlated-k parameters. Scattering parameters, blackbody and solar functions are cast as compact k-dependent source
terms and used in the radiance computations. Preliminary transmittance results are within 2% of MODTRAN with a
two-orders-of-magnitude computational savings. Preliminary radiance computations in the visible spectrum are within a
few percent of MODTRAN results, but with orders of magnitude speed up over comparable MODTRAN runs. This new
RT capability has potential applications for hyperspectral scene simulations as well as target acquisition algorithms for
near earth scenarios.
We describe improvements to a recently developed VNIR-SWIR atmospheric correction method for hyper- and multispectral imagery, dubbed QUAC (QUick Atmospheric Correction). It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The newest implementation of QUAC is based on the assumption that the average reflectance of a collection of diverse material spectra, such as the endmember spectra in a scene, is effectively scene independent. This enables the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications on aircraft and spacecraft. QUAC is applied to a diverse collection of hyper- and multispectral data sets and the results are compared to those obtained with the physics-based atmospheric correction code FLAASH (Fast Line of sight Atmospheric Analysis of Spectral Hypercubes).
The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances. Validations of the new band model algorithms against line-by-line (LBL) codes have proven successful.
Atmospheric Correction Algorithms (ACAs) are used in applications of remotely sensed Hyperspectral and Multispectral Imagery (HSI/MSI) to correct for atmospheric effects on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is a forward-model based ACA created for HSI and MSI instruments which operate in the visible through shortwave infrared (Vis-SWIR) spectral regime. Designed as a general-purpose, physics-based code for inverting at-sensor radiance measurements into surface reflectance, FLAASH provides a collection of spectral analysis and atmospheric retrieval methods including: a per-pixel vertical water vapor column estimate, determination of aerosol optical depth, estimation of scattering for compensation of adjacency effects, detection/characterization of clouds, and smoothing of spectral structure resulting from an imperfect atmospheric correction. To further improve the accuracy of the atmospheric correction process, FLAASH will also detect and compensate for sensor-introduced artifacts such as optical smile and wavelength mis-calibration. FLAASH relies on the MODTRANTM radiative transfer (RT) code as the physical basis behind its mathematical formulation, and has been developed in parallel with upgrades to MODTRAN in order to take advantage of the latest improvements in speed and accuracy. For example, the rapid, high fidelity multiple scattering (MS) option available in MODTRAN4 can greatly improve the accuracy of atmospheric retrievals over the 2-stream approximation. In this paper, advanced features available in FLAASH are described, including the principles and methods used to derive atmospheric parameters from HSI and MSI data. Results are presented from processing of Hyperion, AVIRIS, and LANDSAT data.
A new sensor-specific correlated-k (c-k) ultra-fast radiative transfer (RT) formalism, kURT, has been designed for fast broad-bandpass scene simulations from UV-visible to LWIR wavelengths. A higher resolution RT code (1 cm-1 MODTRAN) has been adapted to output 1 cm-1 correlated-k parameters for ozone, water, and the combined uniformly mixed species on a pressure-temperature grid, which are merged to form a compact c-k set incorporating the sensor bandpass response function. The compact set is used to compute bandpass transmittance and radiance in near-real time. Scattering parameters (molecular Rayleigh, clouds and aerosols), blackbody and solar functions are cast as compact k-dependent source terms and used in the radiance computations. Preliminary transmittance results for 3-5 and 8-12 micron bandpasses and visible-MWIR sensors yield results within 2% of a 1 cm-1 MODTRAN calculation with a two-orders-of-magnitude computational savings. Applications include near-earth broadband propagation and extinction calculations for target detection and recognition, mid-range tracking, and search and rescue operations from ground and low altitude aircraft.
First-principles atmospheric correction of earth-viewing spectral imagery requires atmospheric property information derived from the image itself or measured independently. A field experiment was conducted in May, 2003 at Davis, CA to investigate the validity and consistency of atmospheric properties and surface reflectances derived from simultaneous ground-, aircraft- and satellite-based spectral measurements. The experiment involved the simultaneous collection of HyMap and Landsat-7 imagery, in-situ reflectance spectra of calibration surfaces, and sun and sky radiances from ultraviolet and visible multi-filter rotating shadowband radiometers (MFRSRs). This paper briefly describes the experiment, data analysis and key results.
We describe a new visible-near infrared short-wavelength infrared (VNIR-SWIR) atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. QUAC is applied to atmospherically correction several AVIRIS data sets and a Landsat-7 data set, as well as to simulated HyMap data for a wide variety of atmospheric conditions. Comparisons to the physics-based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) code are also presented.
The MODTRANTM5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRANTM atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.
The MODTRAN5(1a, in press) radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN(tm) atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN(tm) (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.
The MODTRAN5(TM) (1a, in press) radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN(TM) atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN(TM) (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.
KEYWORDS: Atmospheric modeling, Sensors, Reflectivity, Monte Carlo methods, Atmospheric sensing, Thermography, Algorithm development, 3D modeling, Infrared radiation, Long wavelength infrared
This paper demonstrates the use of a high fidelity hyperspectral scene simulation tool, called MCScene, to generate realistic thermal infrared scenes that can be used for algorithm development efforts, such as gas plume detection algorithms. MCScene is based on a Direct Simulation Monte Carlo (DSMC) approach for modeling 3D atmospheric
radiative transport, as well as spatially inhomogeneous surfaces including surface BRDF effects. Synthetic “groundtruth” is specified as surface and atmospheric property inputs, and it is practical to consider wide variations of these properties. The model includes treatment of land and ocean surfaces, 3D terrain and bathymetry, 3D surface objects, and effects of finite clouds with surface shadowing. The computed hyperspectral data cubes can supplement field validation data for algorithm development. Sample calculations presented in this paper include a thermal infrared simulation for a
desert scene that includes a gas plume produced by an industrial complex. This scene was derived from an AVIRIS visible to SWIR HSI data collect over the Virgin Mountains in Nevada. The data has been extrapolated to the thermal IR and a representative industrial site and plume have been added to the scene.
FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) is a first-principles atmospheric correction algorithm for visible to shortwave infrared (SWIR) hyperspectral data. The algorithm consists of two main steps. The first is retrieval of atmospheric parameters, visibility (which is related to the aerosol type and distribution) and column water vapor. The second step is solving the radiation transport equation for the given aerosol and column water and transformation to surface reflectance. The focus of this paper is on the FLAASH water vapor retrieval algorithm. Modeled radiance values in the spectral region of one water vapor absorption feature are calculated from MODTRAN 4 using several different water vapor amounts and are used to generate a Look-Up Table (LUT). The water band typically used is 1130 nm but either the 940 or 820 nm band may also be used. Measured radiance values are compared to the LUT to determine the column water vapor amount for each pixel in the scene. We compare the results of water retrievals for each of these bands and also the results of their corresponding reflectance retrievals.
KEYWORDS: Monte Carlo methods, Atmospheric modeling, 3D modeling, Clouds, Reflectivity, Photons, Hyperspectral simulation, Scattering, Long wavelength infrared, Data modeling
The MCScene code, a high fidelity model for full optical spectrum (UV to LWIR) hyperspectral image (HSI) simulation, will be discussed and its features illustrated with sample calculations. MCScene is based on a Direct Simulation Monte Carlo approach for modeling 3D atmospheric radiative transport, as well as spatially inhomogeneous surfaces including surface BRDF effects. The model includes treatment of land and water surfaces, 3D terrain, 3D surface objects, and effects of finite clouds with surface shadowing. This paper will review the more recent upgrades to the model, including the development of an approach for incorporating direct and scattered thermal emission predictions into the MCScene simulations. Calculations presented in the paper include a full optical spectrum simulation from the visible to the LWIR for a desert scene. This scene was derived from an AVIRIS visible to SWIR HSI data collect over the Virgin Mountains in Nevada, extrapolated to the thermal IR. Other calculations include complex 3D clouds over urban and rural terrain.
The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.
KEYWORDS: Monte Carlo methods, Reflectivity, 3D modeling, Atmospheric modeling, Photons, Clouds, Sensors, Scene simulation, Hyperspectral simulation, RGB color model
Spectral Sciences, Inc., in collaboration with NASA and AFRL, are developing a high fidelity model for hyperspectral image (HSI) simulation. The simulation is based on a Direct Simulation Monte Carlo (DSMC) approach for modeling topographic effects. Synthetic “ground-truth” is specified as surface and atmospheric property inputs, and it is practical to consider wide variations of these properties. The model includes treatment of land and ocean surfaces, 3D terrain and bathymetry, 3D surface objects, and effects of finite clouds with surface shadowing. The computed HSI data cubes can serve as both a surrogate for and a supplement to field validation data for algorithm development efforts or for sensor design trade-studies. The initial version of the software package developed in collaboration with NASA treated the reflective spectral domain from the visible to the SWIR. In this paper, we review the reflective spectral domain model and present our approach for extending the HSI scene simulation package into the thermal infrared. The model is demonstrated with a variety of Visible and LWIR scene simulations.
With its combination of good spatial and spectral resolution, visible to near infrared spectral imaging from aircraft or spacecraft is a highly valuable technology for remote sensing of the earth's surface. Typically it is desirable to eliminate atmospheric effects on the imagery, a process known as atmospheric correction. In this paper we review the basic methodology of first-principles atmospheric correction and present results from the latest version of the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) algorithm. We show some comparisons of ground truth spectra with FLAASH-processed AVIRIS data, including results obtained using different processing options, and with results from the ACORN algorithm that derive from an older MODTRAN4 spectral database.
A new matched filter-based algorithm has been developed for detecting and approximately correcting for shadows or other illumination variations in spectral imagery. Initial evaluations have been conducted with a handful of data cubes, including AVIRIS data. The de-shadowed images have a generally realistic appearance and reveal a wealth of previously hidden surface details.
Hyperspectral imagery (HSI) of the ocean-land interface, known as the littoral zone (LZ) can provide a valuable source of information for identification of underwater objects and materials, determination of water depth, and retrieval of water composition. The first step in the analysis is removal of atmospheric effects, resulting in surface reflectance spectra. The atmospheric removal is accomplished with a new version of the MODTRAN-based FLAASH correction code. When available, infrared wavelengths are used to retrieve water vapor and aerosol parameters for the correction and to remove foam and glitter components to yield water-leaving reflectance. A visible-only spectral unmixing technique for foam and glitter removal has also been developed. Bathymetry algorithms that use the 500-700 nm region were developed based on Monte Carlo-simulated "ground truth" spectra. The end-to-end data analysis process has been demonstrated with publicly available AVIRIS imagery.
Terrain categorization and target detection algorithms applied to Hyperspectral Imagery (HSI) typically operate on the measured reflectance (of sun and sky illumination) by an object or scene. Since the reflectance is a non-dimensional ratio, the reflectance by an object is nominally not affedted by variations in lighting conditions. Atmospheric Correction (also referred to as Atmospheric Compensation, Characterization, etc.) Algorithms (ACAs) are used in application of remotely sensed HSI datat to correct for the effects of atmospheric propagation on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is an ACA created for HSI applications in the visible through shortwave infrared (Vis-SWIR) spectral regime. FLAASH derives its physics-based mathematics from MODTRAN4.
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.
MODTRAN4, version 2, will soon be released by the U.S. Air Force Geophysics Laboratory; it is an extension of the MODTRAN4, v1, atmospheric transmission, radiance and flux model developed jointly by the Air Force Research Laboratory / Space Vehicles Directorate (AFRL / VS) and Spectral Sciences, Inc. The primary accuracy improvements in MODTRAN4 remain those previously published: (1) the multiple scattering correlated-k approach to describe the statistically expected transmittance properties for each spectral bin and atmospheric layer, and (2) the Beer-Lambert formulation that improves the treatment of path inhomogeneities. Version 2 code enhancements are expected to include: *pressure-dependent atmospheric profile input, as an auxiliary where the hydrostatic equation is integrated explicitly to compute the altitudes, *CFC cross-sections with band model parameters derived from pseudo lines, *additional pressure-induced absorption features from O2, and *a new 5 cm-1 band model option. Prior code enhancements include the incorporation of solar azimuth dependence in the DISORT-based multiple scattering model, the introduction of surface BRDF (Bi-directional Radiance Distribution Functions) models and a 15 cm-1 band model for improved computational speed. Last year's changes to the HITRAN database, relevant to the 0.94 and 1.13 micrometers bands of water vapor, have been maintained in the MODTRAN4,v2 databases.
Atmospheric emission, scattering, and photon absorption degrade spectral imagery data and reduce its utility. The Air Force Research Laboratory and Spectral Sciences, Inc. are developing a MODTRAN4-based 'atmospheric mitigation’ algorithm to support current and planned IR-visible-UV sensor spectral radiance imagery measurements. The intent is to provide surface reflectance and emissivity imagery data of sufficient accuracy for input into subsequent analyses of surface properties, effectively removing the atmospheric component. This report is the result of the application of the atmospheric mitigation algorithm to a NASA/JPL AVIRIS spectral image cube as a pre-processing step towards improving the performance of image categorization routines.
MODTRAN4, the newly released version of the U.S. Air Force atmospheric transmission, radiance and flux model is being developed jointly by the Air Force Research Laboratory / Space Vehicles Directorate (AFRL / VS) and Spectral Sciences, Inc. It is expected to provide the accuracy required for analyzing spectral data for both atmospheric and surface characterization. These two quantities are the subject of satellite and aircraft campaigns currently being developed and pursued by, for instance: NASA (Earth Observing System), NPOESS (National Polar Orbiting Environmental Satellite System), and the European Space Agency (GOME - Global Ozone Monitoring Experiment). Accuracy improvements in MODTRAN relate primarily to two major developments: (1) the multiple scattering algorithms have been made compatible with the spectroscopy by adopting a correlated-^ approach to describe the statistically expected transmittance properties for each spectral bin and atmospheric layer, and (2) radiative transfer calculations can be conducted with a Beer-Lambert formulation that improves the treatment of path inhomogeneities. Other code enhancements include the incorporation of solar azimuth dependence in the DISORT-based multiple scattering model, the introduction of surface BRDF (Bi-directional Radiance Distribution Functions) models and a 15 cm-1 band model for improved computational speed. Finally, recent changes to the HITRAN data base, relevant to the 0.94 and 1.13 um bands of water vapor, have been incorporated into the MODTRAN4 databases.
This paper presents an overview of the latest version of a MODTRAN4-based atmospheric correction (or "compensation") algorithm developed by Spectral Sciences, Inc. and the Air Force Research Laboratory for spectral imaging sensors. New upgrades to the algorithm include automated aerosol retrieval, cloud masking, and speed improvements. In addition, MODTRAN4 has been updated to correct recently discovered errors in the HITRAN-96 water line parameters. Reflectance spectra retrieved from AVIRIS data are compared with "ground truth" measurements, and good agreement is found.
MODTRAN4, the newly released version of the U.S. Air Force atmospheric transmission, radiance and flux model is being developed jointly by the Air Force Research Laboratory/Space Vehicles Directorate and Spectral Sciences, Inc. It is expected to provide the accuracy required for analyzing spectral data for both atmospheric and surface characterization. These two quantities are the subject of satellite and aircraft campaigns currently being developed and pursued by, for instance: NASA (Earth Observing System), NPOESS (National Polar Orbiting Environmental Satellite System), and the European Space Agency (GOME--Global Ozone Monitoring Experiment). Accuracy improvements in MODTRAN relate primarily to two major developments: (1) the multiple scattering algorithms have been made compatible with the spectroscopy by adopting a corrected-k approach to describe the statistically expected transmittance properties for each spectral bin and atmospheric layer, and (2) radiative transfer calculations can be conducted with a Beer-Lambert formulation that improves the treatment of path inhomogeneities. Other code enhancements include the incorporation of solar azimuth dependence in the DISORT- based multiple scattering model, the introduction of surface BRDF (Bi-directional Radiance Distribution Functions) models and 15 cm-1 band model for improved computational speed.
A new, state-of-the-art atmospheric correction algorithm for the solar spectral range has been developed based on the MODTRAN4 code. The primary data products are surface reflectance spectra, column water vapor maps and relative surface elevation maps. In addition, a radiance simulation tool, an automated visibility retrieval algorithm and a spectral 'polishing' algorithm are included. Validations of retrievals have been carried out by analyzing data that encompass a variety of atmospheric and surface conditions. Some results and their implications for atmospheric correction and spectroscopy are discussed.
Scattered solar radiance from cirrus clouds has traditionally been detected over land at 1.37 micrometer, a wavelength that is ordinarily opaque to the surface due to water vapor absorption. We describe a new pairwise regression method for spectral imagery that retrieves cloud signals in the vicinity of a partially transmitting band, such as the 1.13 micrometer band, over any type of spatially structured terrain. The method, which uses spatial filtering and linear regression to cancel the surface background, has been applied to several rural and urban AVIRIS scenes. With a single cloud or cloud layer in the scene, the 1.13 micrometer and 1.37 micrometer cloud signals are closely correlated. Since the two signals are absorbed differently by water vapor, the slope of the correlation plot indicates the column water vapor above the cloud and thus the approximate cloud altitude. The less strongly absorbed 1.13 micrometer signal is closely related to the cloud optical thickness and can be used by itself or in combination with the 1.37 micrometer signal to correct apparent surface reflectance spectra for cirrus cloud effects.
Atmospheric emission, scattering, and photon absorption degrade spectral imagery data and reduce its utility. We report on the use of an atmospheric compensation code for the visible and near-infrared, based on MODTRAN 4, that includes spectral analysis, accounts for interference to a given pixel by adjacent pixels, and provides a polishing routine to clear residual atmospheric spectral features common to a group of pixels. A NASA/JPL AVIRIS data sample is analyzed.
MODTRAN4, the latest publicly released version of MODTRAN, provides many new and important options for modeling atmospheric radiation transport. A correlated-k algorithm improves multiple scattering, eliminates Curtis-Godson averaging, and introduces Beer's Law dependencies into the band model. An optimized 15 cm-1 band model provides over a 10-fold increase in speed over the standard MODTRAN 1 cm-1 band model with comparable accuracy when higher spectral resolution results are unnecessary. The MODTRAN ground surface has been upgraded to include the effects of Bidirectional Reflectance Distribution Functions (BRDFs) and Adjacency. The BRDFs are entered using standard parameterizations and are coupled into line-of-sight surface radiance calculations.
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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