Raymond Haren

Tunable narrow-band filter for LWIR hyperspectral imaging

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.

Extraction of spatial and spectral scene statistics for hyperspectral scene simulation

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.

Full optical spectrum hyperspectral scene simulation

Abstract - Full optical spectrum (UV to LWIR) hyperspectral scene simulation provides an accurate, robust, and efficient means for algorithm validation and sensor design trade studies. This paper reviews the development of a first-principles, high-fidelity HSI/MSI image simulation capability, dubbed MCScene and demonstrates how the model can be used for sensor design trade studies. MCScene incorporates all optical effects important for solar-illuminated and thermal scenes, including molecular and aerosol scattering, absorption and emission, surface scattering and emission with material-dependent bidirectional reflectance distribution functions (BRDFs), multiple scattering events, surface adjacency effects, and scattering, emission and shading by clouds, for arbitrary solar illumination and sensor viewing geometries. The “world” of the simulation is a cube that encloses a user-definable atmosphere containing molecular species, aerosols, and clouds, and a terrain representing the ground. The sensor spatial and spectral resolution, its location, and the viewing angle are also specified. 3D objects can also be inserted into the scene. A particular strength of MCScene is that a simulation can be data driven. Terrain information can be imported from USGS digital elevation maps. Surface reflectance or emissivity/temperature maps can be derived from collected imagery, thus incorporating natural spectral and spatial texturing into a simulation. Basic features of the model will be discussed and illustrated with a full spectrum simulation for a prototype hyperspectral sensor.

Monte Carlo based hyperspectral scene simulation

This paper will discuss recent improvements made to the Monte Carlo Scene (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 (DSMC) 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 bidirectional reflectance distribution function (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 and both spectral and spatial properties of simulations of 3-dimensional cloud fields will also be presented.

Improved full spectrum cloudy scene simulation

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.

Improved Full Spectrum Cloudy Scene Simulation

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.

Fast Monte Carlo full spectrum scene simulation

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.

Extraction of hyperspectral scene statistics and scene realization

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.

Thermal infrared scene simulation for plume detection algorithm evaluation

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.

Full-spectrum scene simulation

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.