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.
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.
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.
Vehicles concealed in highly-cluttered, vegetated scene environments pose significant challenges for passive sensor systems and algorithms. System analysts working hypersectral exploitation research require and at-aperature simulation capability that allows them to reliably investigate beyond ther highly-limited scenarios that expensive field data sets provide. To be useful to the analyst, such a simulation should address the following requirements: (1) the ability to easily generate scene representations for abritrary Earth regions of tactical interests; (2) the ability to represent scene components, like terrain, trees and bushes, to an extremely high spatial resolution for calculation of accurate multiple spectral reflections, occlusions and shadowing; (3) the ability to stimulate the 3D scene with realistic natural irradiances for arbitrary model atmospheres; (4) the ability to appropriately integrate improving, rigorous thermal, spectral signature and atmospheric propogation models; (5) the ability to effectively render at-apurature hyperspectral data sets in a reasonable run-time. herein the authors describe their continuing work toward a comprehensive ray-tracer-based simulation archetecture and prototype capability that addresses these requirements, with emphasis on new techniques for high fidelity thermal modeling, and recent improvements in atmospherically scattered irradiance modeling, manmade light source modeling, and GIS-based database generation, including automated material classification of terrain and scene elements.
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.
Vehicles concealed in highly cluttered, vegetated scene environments pose significant challenges for passive sensor systems and algorithms. System analysts working hyperspectral exploitation research require an at-aperture simulation capability that allows them to reliably investigate beyond the highly-limited scenarios that expensive field data sets afford.
To be useful to the analyst, such a simulation should address the following requirements: (1) the ability to easily generate scene representations for arbitrary Earth regions of tactical interest; (2) the ability to represent scene components, like terrain, trees and bushes, to an extremely high spatial resolution for calculation of accurate multiple spectral reflections, occlusions and shadowing; (3) the ability to stimulate the 3D scene with realistic natural spectral irradiances for arbitrary 3D model atmospheres; (4) the ability to appropriately integrate constantly improving, rigorous thermal, spectral signature and atmospheric propagation models; (5) the ability to efficiently render at-aperture hyperspectral data sets in a reasonable run-time.
Herein the authors describe their work toward a comprehensive ray-tracer-based simulation architecture and prototype capability that addresses these requirements. They describe their development of a GIS-based toolset for database generation, tools for 3D vegetated terrain-model development, and a prototype raytracer-based spectral scene generator.
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.
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.
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.
IR sensing has been a key enabling technology in military systems providing advantages in night vision, surveillance, and ever more accurate targeting. Passive hyperspectral imagin, the ability to gather and process IR spectral information from each pixel of an IR image, can ultimately provide 2D composition maps of a scene under study. FInding applications such as atmospheric, and geophysical remote sensing, camouflaged target recognition, and defence against chemical weapons.
Analysis of 68 condemned LANTIRN navigation pod FLIR windows was undertaken to determine the nature and extent of damage to these windows. Visual and low-magnification examinations using reflected and transmitted light conditions were performed, as well as profilometry and scanning electron microscopy (SEM) examination on selected specimens. A number of primary modes were found which accounted for the majority of the failures seen in this population of windows. These modes were: high energy impacts due to large objects such as hail, birds, and runway debris; interaction of the residual stress state at the interface of the bulk ZnSe/ZnS coating with rain and bug strikes; and opacification due to sand erosion and atmospheric etching. Machining damage and misoriented window installation were also found. Windows which had seen appreciable hours of service were almost completely devoid of AR coating on the forward face. A navigation pod, which houses the window, was also obtained to determine if the window installation contributed to the causes of failure. Suggestions to improve the reliability of the present window material were listed.
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