Hyperspectral imagery was taken of four vehicles from a roof at the Rochester Institute of Technology (RIT) at various vehicle orientations in illumination conditions dominated by direct solar radiation in order to explore and model the in-scene bidirectional reflectance distribution functions (BRDFs) of 3D objects. The four vehicles were rotated and imaged through the span of six hours resulting in many combinations of vehicle orientation, source azimuth, and source zenith. In addition to the general sampling of vehicle BRDFs, three experiments were designed and executed in order to understand the contributions of vehicle shape, vehicle color, and background on the observed in-scene BRDFs.
KEYWORDS: Polarimetry, 3D modeling, Polarization, Data modeling, Radiative transfer, Monte Carlo methods, Scene simulation, 3D modeling, Radiative transfer, Scene simulation, Scattering, Sensors, Aerosols
A validated, polarimetric 3-dimensional simulation capability, P-MCScene, is being developed by generalizing Spectral Sciences’ Monte Carlo-based synthetic scene simulation model, MCScene, to include calculation of all 4 Stokes components. P-MCScene polarimetric optical databases will be generated by a new version (MODTRAN7) of the government-standard MODTRAN radiative transfer algorithm. The conversion of MODTRAN6 to a polarimetric model is being accomplished by (1) introducing polarimetric data, by (2) vectorizing the MODTRAN radiation calculations and by (3) integrating the newly revised and validated vector discrete ordinate model VDISORT3. Early results, presented here, demonstrate a clear pathway to the long-term goal of fully validated polarimetric models.
KEYWORDS: Cameras, Fourier transforms, Signal detection, Image filtering, Video, Sensors, Remote sensing, Signal to noise ratio, Modulation, Principal component analysis
We present progress being made in the passive optical remote detection of ground surface vibration. With proper design, minute seismic surface waves may be captured using remote visible imagery. The utility of subband steerable filters to the detection of surface vibrations in the absence of inherent image contrast is demonstrated. Detections with the filters are shown with laboratory data and compared to Fourier transform results over a range of surface vibrational amplitudes. We present an analysis of the optical measurements of ground surfaces performed during the passing of nearby trains with discussion of the hardware, software, and detection clutter sources. Results from optical remote sensing are interpreted using additional accelerometer measurements and image processing.
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.
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.
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.
This paper discusses recent advances in the simulation of spectral scenes with partial cloud cover. We examine the effect
of broken cloud fields on the solar illumination reaching the ground. Application of aerosol retrieval techniques in the
vicinity of broken clouds leads to significant over-prediction of aerosol optical depth because of the enhancement of
visible illumination due to scattering of photons from clouds into clear patches. These illumination enhancement effects
are simulated for a variety of broken cloud fields using the MCScene code, a high fidelity model for full optical
spectrum (UV through LWIR) spectral image simulation. MCScene provides an accurate, robust, and efficient means to
generate spectral scenes for algorithm validation. MCScene utilizes a Direct Simulation Monte Carlo approach for
modeling 3D atmospheric radiative transfer (RT), including full treatment of molecular absorption and Rayleigh
scattering, aerosol absorption and scattering, and multiple scattering and adjacency effects, as well as scattering from
spatially inhomogeneous surfaces.
The MCScene code, a high fidelity model for full optical spectrum (UV to LWIR) spectral image simulation, will be
discussed and its features illustrated with sample calculations. The MCScene simulation 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 ocean 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. Sample calculations presented in the paper include a full optical spectrum simulation from the
visible to the LWIR for a desert scene under a broken cloud field. This scene was derived from an AVIRIS visible to
SWIR spectral imaging data collect over the Virgin Mountains in Nevada. The data has been extrapolated to the thermal
IR. Other calculations include complex 3D clouds over urban and rural terrain.
KEYWORDS: Clouds, Monte Carlo methods, Atmospheric modeling, Photons, 3D modeling, Scattering, Aerosols, Reflectivity, Scene simulation, Atmospheric particles
This paper discusses the effects of broken cloud fields on solar illumination reaching the ground. Application of aerosol
retrieval techniques in the vicinity of broken clouds leads to significant over prediction of aerosol optical depth because
of the enhancement of visible illumination from the scattering of photons from clouds into clear patches. These
illumination enhancement effects are simulated for a variety of broken cloud fields using the MCScene code, a high
fidelity model for full optical spectrum (UV through LWIR) spectral image simulation. MCScene provides an accurate,
robust, and efficient means to generate spectral scenes for algorithm validation. MCScene utilizes a Direct Simulation
Monte Carlo approach for modeling 3D atmospheric radiative transfer (RT), including full treatment of molecular
absorption and Rayleigh scattering, aerosol absorption and scattering, and multiple scattering and adjacency effects, as
well as scattering from spatially inhomogeneous surfaces. The model includes treatment of land and ocean surfaces, 3D
terrain, 3D surface objects, and effects of finite clouds with surface shadowing. The paper includes an overview of the
MCScene code and a series of calculations for broken 3D cloud fields demonstrating the effects of clouds on downwelling
flux.
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.
This paper will discuss recent improvements made to the MCScene code, a high fidelity model for full optical spectrum
(UV through LWIR) hyperspectral image (HSI) simulation. MCScene provides an accurate, robust, and efficient means
to generate HSI scenes for algorithm validation. MCScene utilizes a Direct Simulation Monte Carlo approach for
modeling 3D atmospheric radiative transfer (RT) including full treatment of molecular absorption and Rayleigh
scattering, aerosol absorption and scattering, and multiple scattering and adjacency effects, as well as scattering from
spatially inhomogeneous surfaces, including surface BRDF effects. The model includes treatment of land and ocean
surfaces, 3D terrain, 3D surface objects, and effects of finite clouds with surface shadowing. This paper will provide an
overview of how RT elements are incorporated into the Monte Carlo engine. Several new examples of the capabilities
of MCScene to simulate 3-dimensional cloud fields will also be discussed, and sample calculations will be presented.
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.
KEYWORDS: Monte Carlo methods, 3D modeling, Hyperspectral simulation, Atmospheric modeling, Clouds, Reflectivity, Sensors, Photons, RGB color model, Computer simulations
This paper discusses the formulation and implementation of an acceleration approach for the MCScene code, a high
fidelity model for full optical spectrum (UV to LWIR) hyperspectral image (HSI) simulation. The MCScene simulation
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 ocean
surfaces, 3D terrain, 3D surface objects, and effects of finite clouds with surface shadowing. This paper will review an
acceleration algorithm that exploits spectral redundancies in hyperspectral images. In this algorithm, the full scene is
determined for a subset of spectral channels, and then this multispectral scene is unmixed into spectral end members and
end member abundance maps. Next, pure end member pixels are determined at their full hyperspectral resolution, and
the full hyperspectral scene is reconstructed from the hyperspectral end member spectra and the multispectral abundance
maps. This algorithm effectively performs a hyperspectral simulation while requiring only the computational time of a
multispectral simulation. The acceleration algorithm will be demonstrated, and errors associated with the algorithm will
be analyzed.
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.
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.
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.
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.
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.
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, 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.
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.
KEYWORDS: Signal processing, Signal detection, Sensors, Process control, Semiconductor lasers, Absorption, Calibration, Gases, Temperature metrology, Environmental monitoring
An ammonia monitor designed for in situ smoke stack or exhaust duct applications is discussed here. A probe composed of a diffusion cell with a protected multipass optical measurement cavity provides the optical interaction with the sample. Other components of the system include signal processing electronics and an embedded PC104 computer platform. This instrument is useful in a wide variety of ammonia monitoring and process control applications, particularly ammonia-based NOx control technologies, such as selective catalytic reduction (SCR) and selective non-catalytic reduction (SNCR). The in situ design eliminates sample handling problems, associated with extractive analysis of ammonia, such as sample line adsorption and heated sample trains and cells. The sensor technology exploited in this instrument is second harmonic spectroscopy using a near infrared diode laser. Data collected during field trials involving both SCR and SNCR applications demonstrate the feasibility and robust operation of this instrument in traditionally problematic operating environments. The instrument can measure other gases by changing the wavelength, either by changing the diode operational set point or by changing the diode. In addition, with straightforward modification the instrument can measure multiple species.
Measurements made using two different types of ammonia monitors during a two-month field study in the summer of 1994 are discussed. The first instrument was a diode-laser based open path monitor designed for automated operation in an industrial environment. The second is a point monitor based on thermal decomposition of ammonia to NO and subsequent analysis by O3 - NO chemiluminescence. The two monitors provided consistent measurements of ammonia during weeks of continuous unattended operation.
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