This paper examines the utility of synthetic spectral imagery derived from visible-SWIR HSI or MSI sensor data. Our method uses an endmember matching approach to extend spectral information to wavelengths beyond the original imagery. The derived images exhibit some loss of spectral and spatial clutter, but the dimensionality may be sufficient for applications such as synthetic scene simulations and development of algorithms for object detection.

Targets located on the Precision Impact Range Area (PIRA) of Edwards AFB are used to evaluate imaging systems' sensitivity and spatial resolution to ensure they meet specified requirements. Spectral Sciences, Inc., is developing a fieldready electro-optical sensor calibration/test system for airborne instruments from the visible through longwave infrared. This spectral region is particularly challenging because of the contributions from both solar and thermal fluxes. The system is composed of spectral-spatial ground targets and atmospheric characterization instruments. The design challenges for a new ground target installation applicable over short to long ranges and a broad optical spectrum include: 1) development of an innovative spectral-spatial, high contrast, high uniformity, knife edge target for determination of the spatial characteristics of the imaging system under test, such as the modulation transfer function (MTF) and relative edge response (RER), noise equivalent temperature difference (NETD), linearity and more; 2) development and implementation of a suite of auxiliary instruments to quantify the atmospheric effects, such as line-of-sight (LOS) turbulence, surface temperatures, humidity, and visibility; 3) development of targets with stable, quantifiable spectral response that can be used for evaluation for the spectral characteristics of multi- or hyperspectral imaging systems; and 4) engineering the target set for simplified long-term maintenance and durability. In this paper we report on the development of a prototype 2m by 2m thermally controlled knife edge target. The target is composed of four 1m by 1m panels each of which has independent temperature control and face surface materials which can be exchanged with other panel faces to produce patterns or spectral features. The full prototype system can be rotated and tipped to maximize the surface area apparent to a sensor system under test. The paper includes initial field measurements of the target array using visible, MWIR and LWIR imaging systems.

Development of algorithms for remote sensing applications can be facilitated with accurate scene simulations, where terrain reflectance and topography as well as all atmospheric and illumination conditions are controlled by the investigator. One such scene simulation model is MCScene. The MCScene model 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. Simulations can be performed from the UV through the LWIR. In this paper, we illustrate the use of MCScene as a tool in remote sensing algorithm development by simulating a partly cloudy scene and using this scene to test and evaluate a spectral cloud masking algorithm.

We demonstrate an enhanced ACE target detection algorithm for poorly illuminated and shadowed pixels in which image segmentation based on illumination is performed prior to target detection. This enhanced ACE detection method (ACE-shadow) relies on FLAASH-based scene atmospheric correction and MODTRAN-calculated direct to diffuse illumination ratios to model shadowed target spectra. Improvements to target detection were realized using our ACE-shadow detection algorithm because SNR characteristics of shadowed pixels dominate the segmented covariance.

With the growing number of inactive and active man-made space objects orbiting Earth, space situational awareness (SSA) is becoming an ever more critical element of both national security and the commercial use of space. Accurate tracking and identification of resident space objects (RSO) through space-based or ground-based imagery can provide a means of characterizing potential threats to our orbital assets, and possibly to infer the intent of foreign objects. This work discusses the development of a new satellite identification tool that employs physical model predictions and deep learning neural networks (NN) to increase the quantity of information that can be extracted from these sources of space surveillance imagery. When observing distant objects in geo-stationary or midcourse orbits, or small satellites in low orbits, unresolved imagery with less than a dozen resolution elements may be the only available optical measurements. Our satellite identification method, the Cognitive Image Recovery Code (CIRC), is designed to evaluate low resolution imagery, including views from multiple locations and temporal light curves generated from unresolved imagery, and to predict the most likely RSO configuration from a list of possible models. For a number of viewing and illumination angles, the tool applies a dense neural network to identify characteristic features of each model and evaluates the most likely match to an unresolved observation. The training imagery for the machine learning algorithm was generated using QUID (QUick Image Display), our fast, first-principles signature simulation code, and a catalog of physically attributed 3D models of various satellites and RSO types. The fidelity of these simulations ensures that the training imagery is both realistic and radiometrically accurate, and the computation speed generates images onthe-fly, allowing an iterative refinement of the model prediction.

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.

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.

This study examines the influence of non-Lambertian reflectance effects on the detection of subpixel vehicles in hyperspectral imagery. Object-level BRDF spectral signatures for an olive green sedan were simulated using a fast, radiometrically accurate 3D rendering model, and the signatures were embedded as subpixel targets of low fractional fill into the HyMap hyperspectral image provided in the Rochester Institute of Technology Blind Test dataset. Detection algorithms based on the ACE detector were run on the scene. The results demonstrate a significant improvement in detection performance when including the target's BRDF variation in the detection scheme through either a subspace ACE or a Bayesian selection (multiple ACE detector) method.

The MODTRAN6 radiative transfer (RT) code is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. This version of the code incorporates modern software architecture including an application programming interface, enhanced physics features including a line-by-line algorithm, a supplementary physics toolkit, and new documentation. The application programming interface has been developed for ease of integration into user applications. The MODTRAN code has been restructured towards a modular, object-oriented architecture to simplify upgrades as well as facilitate integration with other developers' codes. MODTRAN now includes a line-by-line algorithm for high resolution RT calculations as well as coupling to optical scattering codes for easy implementation of custom aerosols and clouds.

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.

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 (R) 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. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.11.111707]

The QUick Image Display (QUID) model accurately computes and displays radiance images of aircraft and other objects, generically called targets, at animation rates while the target undergoes unrestricted flight. Animation rates are obtained without sacrificing radiometric accuracy by using two important innovations. First, QUID has been implemented using the Open Scene Graph (OSG) library, an open-source, cross-platform 3-D graphics toolkit for the development of high performance graphics applications in the fields of visual simulation, virtual reality, scientific visualization and modeling. Written entirely in standard C++ and fully encapsulating OpenGL and its extensions, OSG exploits modem graphics hardware to perform the computationally intensive calculations such as hidden surface removal, 3-D transformations, and shadow casting. Second, a novel formulation for reflective/emissive terms enables rapid and accurate calculation of per-vertex radiance. The bi-directional reflectance distribution function (BRDF) is a decomposed into separable spectral and angular functions. The spectral terms can be pre-calculated for a user specified band pass and for a set of target-observer ranges. The only BRDF calculations which must be performed during target motion involves the observer-target-source angular functions. QUID supports a variety of target geometry files and is capable of rendering scenes containing high level-of-detail targets with thousands of facets. QUID generates accurate visible to LWIR radiance maps, in-band and spectral signatures. The newest features of QUID are illustrated with radiance and apparent temperature images of threat missiles as viewed by an aircraft missile warning system.

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.

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 MODTRAN TM 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.