Dr. Scott D. Brown Profile

Dr. Scott D. Brown

Associate Scientist

SPIE Involvement:

Author

Publications (49)

Proceedings Article | 7 June 2024 Presentation + Paper

Demonstration of the feasibility of using synthetically generated condensed water vapor plume imagery to train an AI model to automatically segment real imagery

Anna Mason, Ryan Connal, Jacob Irizarry, Byron Eng, Michael Saunders, Adam Goodenough, Scott Brown, Carl Salvaggio

Proceedings Volume 13035, 1303516 (2024) https://doi.org/10.1117/12.3012971

KEYWORDS: Education and training, Data modeling, Image segmentation, RGB color model, Artificial intelligence, Image processing, Visualization, Sensors, Machine learning, Visual process modeling

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Training data for sporadically occurring events or anomalous targets is always an issue leading to an imbalance or under-representation in machine learning tasks. Synthetic data can aid in several different ways, such as the generation of suitable numbers of training images and, consequently, this data is often pre-labeled as part of the synthetic image generation process. The quality of this synthetic data can be questioned as to the efficacy of the task, but there is often an opportunity to look at how that quality affects your ultimate objective. For an overhead imagery task, does one need a complete, physically accurate simulation of all the optical, atmospheric, and sensor properties, or does a ”quick-and-dirty” visible wavelength simulation suffice? For all intents and purposes, this is surely dependent on the task at hand.

When it is impossible or impractical to collect real labeled data, simulated data may be the only option. In active research being conducted by the Digital Imaging and Remote Sensing laboratory in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology, researchers are focusing on the estimation of the volume of condensed water vapor plumes that are generated from mechanical draft cooling towers, at a variety of facilities, using various modalities of remote sensing data from different imaging platforms. Prior research has supported the use of machine learning for plume segmentation and multi-view geometry techniques for three-dimensional reconstruction and subsequent volume estimation. To this point, real imagery has been collected from the ground and small unmanned aircraft systems with the end goal of exploring other potential collection platforms.

This research focuses on the training of a U-Net model to mask and segment these condensed water vapor plumes from other objects in the scene. The U-Net model and segmentation process has been successfully previously applied to real, low-altitude imagery and this research focuses on the use and application of the model through the use of simulated imagery. Several aspects of the simulation are of interest; how physically accurate do the scattering properties of the plume data need to be, how critical is the understanding of in situ meteorological conditions, how dependent is the process on the temporal and geographic variety in the data, how important is scene clutter and background type? The synthetic data used in this study was generated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) simulation environment and used to derive the inference and segmentation model to be tested on real imagery. While the trained artificial intelligence model performed consistently well when evaluated with synthetic imagery, the accuracy seen in the synthetic dataset did not translate into comparable results when evaluated with real imagery. However, the successes seen with the synthetic imagery and instances of real imagery results indicate that this binary classification and subsequent volume estimation is feasible to accomplish with high levels of accuracy in the future.

Proceedings Article | 7 June 2024 Presentation + Paper

Multivariate methods to explore system sensitivities for hyperspectral subpixel target detection

Chase Cañas, John Kerekes, Scott Brown

Proceedings Volume 13031, 1303104 (2024) https://doi.org/10.1117/12.3013806

KEYWORDS: Atmospheric modeling, Target detection, Sensors, Visibility, Interpolation, Hyperspectral target detection, Aerosols, Data modeling, Statistical modeling, Statistical analysis

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Proceedings Article | 30 September 2022 Presentation + Paper

Empirical validation of a hyperspectral systems model for subpixel target detection using data from a new UAS field collection

Chase Cañas, John Kerekes, Emmett Ientilucci, Scott Brown

Proceedings Volume 12235, 122350E (2022) https://doi.org/10.1117/12.2633609

KEYWORDS: Target detection, Systems modeling, Atmospheric modeling, Data modeling, Sensors, Imaging systems, Signal to noise ratio, Hyperspectral target detection, Hyperspectral systems, Bidirectional reflectance transmission function

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Proceedings Article | 17 September 2018 Paper

Integration of optomechanical system models with DIRSIG

Keegan McCoy, John Kerekes, Scott Brown, Adam Goodenough, Rolando Raqueño, Jared Van Cor, Ryan Irvin

Proceedings Volume 10743, 1074304 (2018) https://doi.org/10.1117/12.2320641

KEYWORDS: Stray light, Point spread functions, Systems modeling, Imaging systems, Data modeling, Telescopes, Earth observing sensors, Landsat, Performance modeling, Remote sensing

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Proceedings Article | 17 May 2016 Paper

On validating remote sensing simulations using coincident real data

Mingming Wang, Wei Yao, Scott Brown, Adam Goodenough, Jan van Aardt

Proceedings Volume 9840, 984024 (2016) https://doi.org/10.1117/12.2223455

KEYWORDS: Neon, Remote sensing, Vegetation, Computer simulations, LIDAR, Imaging spectroscopy, Reflectivity, Spectroscopy, Data centers, Spatial resolution

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Proceedings Article | 17 May 2016 Paper

Towards an improved understanding of the influence of subpixel vegetation structure on pixel-level spectra: a simulation approach

Wei Yao, Martin van Leeuwen, Paul Romanczyk, Dave Kelbe, Mingming Wang, Scott Brown, Adam Goodenough, Jan van Aardt

Proceedings Volume 9840, 98401H (2016) https://doi.org/10.1117/12.2224141

KEYWORDS: Vegetation, Spectroscopy, Imaging spectroscopy, Ecosystems, Data modeling, Virtual reality, Point spread functions, 3D modeling, Neon, Hyperspectral simulation

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The planned NASA Hyperspectral Infrared Imager (HyspIRI) mission, equipped with an imaging spectrometer that has the capability of monitoring ecosystems globally, will provide an unprecedented opportunity to address scientiﬁc challenges related to ecosystem function and change. However, uncertainty remains around the impact of subpixel vegetation structure, in combination with the point spread function, on pixel-level imaging spectroscopy data. We estimated structural parameters, e.g., leaf area index (LAI), canopy cover, and tree location, from HyspIRI spectral data, with the goal of assessing how subpixel variation in these parameters impact pixel-level imaging spectroscopy data. The ﬁne-scale variability of real vegetation structure makes this a challenging endeavor. Therefore, we utilized a simulation-based approach to counter the time-consuming and often destructive sampling needs of vegetation structural analysis and to simultaneously generate synthetic HyspIRI data pre-launch. Three virtual scenes were constructed, corresponding to the actual vegetation structure in the National Ecological Observatory Network’s (NEON) Paciﬁc Southwest Domain (Fresno, CA). These included an oak savanna, a dense coniferous forest, and a conifer-manzanita-mixed forest. Simulated spectroscopy data for these scenes were then generated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Simulations ﬁrst were used to verify the physical model, virtual scene geometrical information, and simulation parameters. This was followed by simulations of HyspIRI data, where within-pixel structural variability was introduced, e.g., by iteratively changing per-pixel canopy cover and tree placement, tree clustering, leaf area index (LAI), etc., between simulation runs for the virtual scenes. Finally, narrow-band vegetation indices (VIs) were extracted from the data in an attempt to describe the variability of the subpixel structural parameters; this was done in order to assess VI robustness to changes in structural “levels”, as well as placement of trees/canopies within the instrument’s instantaneous ﬁeld-of-view (IFOV). Our ultimate goal is not only to better understand how such subpixel variability inﬂuence imaging spectroscopy outputs, but also to better estimate vegetation structural parameters using spectra. We constructed regression models for LAI (R² = 0.92) and canopy cover (R² = 0.97) with narrow-band VIs via this simulation approach. Our models ultimately are intended to improve the HyspIRI mission’s ability to monitor global vegetation structure.

Proceedings Article | 21 May 2015 Paper

Advances in simulating radiance signatures for dynamic air/water interfaces

Adam Goodenough, Scott Brown, Aaron Gerace

Proceedings Volume 9472, 94720E (2015) https://doi.org/10.1117/12.2177280

KEYWORDS: Monte Carlo methods, Photons, Interfaces, Sensors, Sun, Scattering, Computer simulations, Digital imaging, 3D modeling, Data modeling

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Proceedings Article | 21 May 2015 Paper

Development of land surface reflectance models based on multiscale simulation

Adam Goodenough, Scott Brown

Proceedings Volume 9472, 94720D (2015) https://doi.org/10.1117/12.2177262

KEYWORDS: Photons, Bidirectional reflectance transmission function, Monte Carlo methods, Sensors, 3D modeling, Reflectivity, Data modeling, Performance modeling, Scattering, Transmittance

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Proceedings Article | 24 May 2012 Paper

Using DIRSIG to identify uniform sites and demonstrate the utility of the side-slither calibration technique for Landsat's new pushbroom instruments

Aaron Gerace, John Schott, Scott Brown, Michael Gartley

Proceedings Volume 8390, 83902A (2012) https://doi.org/10.1117/12.919327

KEYWORDS: Sensors, Calibration, Data modeling, Landsat, Digital imaging, Imaging systems, Image sensors, Infrared sensors, Imaging arrays, Earth observing sensors

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Proceedings Article | 9 May 2012 Paper

DIRSIG 5: core design and implementation

Adam Goodenough, Scott Brown

Proceedings Volume 8390, 83900H (2012) https://doi.org/10.1117/12.919321

KEYWORDS: Data modeling, Atmospheric modeling, Systems modeling, Sensors, Atmospheric propagation, 3D modeling, Planets, Chemical elements, Remote sensing, Ray tracing

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Proceedings Article | 14 September 2011 Paper

Modeling the image performance of the Landsat Data Continuity Mission sensors

John Schott, Aaron Gerace, Scott Brown, Michael Gartley

Proceedings Volume 8153, 81530F (2011) https://doi.org/10.1117/12.893675

KEYWORDS: Sensors, Landsat, Data modeling, Infrared sensors, Atmospheric modeling, Signal detection, Calibration, Optical spheres, Imaging systems, Earth observing sensors

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Proceedings Article | 20 May 2011 Paper

Interactive visualization of hyperspectral images on a hyperbolic disk

Adam Goodenough, Ariel Schlamm, Scott Brown, David Messinger

Proceedings Volume 8048, 80481K (2011) https://doi.org/10.1117/12.886927

KEYWORDS: Visualization, Hyperspectral imaging, RGB color model, Image classification, Zoom lenses, Data modeling, Visual analytics, Data centers, Human-machine interfaces, Sensors

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Proceedings Article | 20 May 2011 Paper

Realism, utility, and evolution of remotely sensed simulations

Erin Ontiveros, Michael Gartely, Scott Brown, Rolando Raqueño, David Pogorzala

Proceedings Volume 8048, 80481L (2011) https://doi.org/10.1117/12.884178

KEYWORDS: Data modeling, Computer simulations, Image resolution, 3D modeling, Remote sensing, Visualization, Atmospheric modeling, Digital imaging, Databases, Scene simulation

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Proceedings Article | 20 May 2011 Paper

Modeling space-based multispectral imaging systems with DIRSIG

Scott Brown, Niek Sanders, Adam Goodenough, Michael Gartley

Proceedings Volume 8048, 804814 (2011) https://doi.org/10.1117/12.885540

KEYWORDS: Systems modeling, Data modeling, Imaging systems, Clouds, Clocks, Satellites, Landsat, Sensors, Digital imaging, Earth observing sensors

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The Landsat Data Continuity Mission (LDCM) focuses on a next generation global coverage, imaging system to replace the aging Landsat 5 and Landsat 7 systems. The major difference in the new system is the migration from the multi-spectral whiskbroom design employed by the previous generation of sensors to modular focal plane, multi-spectral pushbroom architecture. Further complicating the design shift is that the reflective and thermal acquisition capability is split across two instruments spatially separated on the satellite bus. One of the focuses of the science and engineering teams prior to launch is the ability to provide seamless data continuity with the historic Landsat data archive. Specifically, the challenges of registering and calibrating data from the new system so that long-term science studies are minimally impacted by the change in the system design. In order to provide the science and engineering teams with simulated pre-launch data, an effort was undertaken to create a robust end-to-end model of the LDCM system. The modeling environment is intended to be flexible and incorporate measured data from the actual system components as they were completed and integrated. The output of the modeling environment needs to include not only radiometrically robust imagery, but also the meta-data necessary to exercise the processing pipeline. This paper describes how the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been utilized to model space-based, multi-spectral imaging (MSI) systems in support of systems engineering trade studies. A mechanism to incorporate measured focal plane projections through the forward optics is described. A hierarchal description of the satellite system is presented including the details of how a multiple instrument platform is described and modeled, including the hierarchical management of temporally correlated jitter that allows engineers to explore impacts of different jitter sources on instrument-to-instrument and band-to-band registration. The capabilities of a new, non-imaging instrument to simulate the measurement of platform ephemeris is also introduced. Finally, the geometric and radiometric foundations for modeling clouds in the DIRSIG model will be described and demonstrated as one of the more significant challenges in registering multi-spectral pushbroom sensor data products.

Proceedings Article | 13 May 2010 Paper

Sub-pixel radiometry: a three-part study in generating synthetic imagery that incorporates sub-pixel variation

Sarah Paul, Adam Goodenough, Scott Brown, Carl Salvaggio

Proceedings Volume 7695, 76950N (2010) https://doi.org/10.1117/12.850180

KEYWORDS: Sensors, Digital imaging, Radiometry, Remote sensing, Hyperspectral imaging, Point spread functions, Systems modeling, Mathematical modeling, Chemical elements, Image resolution

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Proceedings Article | 29 April 2010 Paper

Feature extraction using voxel aggregation of focused discrete lidar data

Shea Hagstrom, David Messinger, Scott Brown

Proceedings Volume 7684, 76840X (2010) https://doi.org/10.1117/12.849675

KEYWORDS: LIDAR, Imaging systems, Remote sensing, Sensors, Clouds, Feature extraction, Signal detection, Digital imaging, Photogrammetry, Associative arrays

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Proceedings Article | 19 April 2010 Paper

A comparison of spatial sampling techniques enabling first principles modeling of a synthetic aperture RADAR imaging platform

Michael Gartley, Adam Goodenough, Scott Brown, Russel Kauffman

Proceedings Volume 7699, 76990N (2010) https://doi.org/10.1117/12.849552

KEYWORDS: Synthetic aperture radar, Antennas, Device simulation, Photons, Bidirectional reflectance transmission function, Radiometry, Reflectivity, Atmospheric modeling, Radar, Scattering

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Proceedings Article | 27 April 2009 Paper

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

John Kerekes, Michael Presnar, Kenneth Fourspring, Zoran Ninkov, David Pogorzala, Alan Raisanen, Andrew Rice, Juan Vasquez, Jeffrey Patel, Robert MacIntyre, Scott Brown

Proceedings Volume 7334, 73340J (2009) https://doi.org/10.1117/12.819265

KEYWORDS: Sensors, Micromirrors, Spectroscopy, Digital micromirror devices, Performance modeling, Video, Data modeling, Mirrors, Molybdenum, Detection and tracking algorithms

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Proceedings Article | 22 April 2009 Paper

Radiometric modeling of mechanical draft cooling towers to assist in the extraction of their absolute temperature from remote thermal imagery

Matthew Montanaro, Carl Salvaggio, Scott Brown, David Messinger, Alfred Garrett, James Bollinger

Proceedings Volume 7299, 729909 (2009) https://doi.org/10.1117/12.817527

KEYWORDS: Sensors, Bidirectional reflectance transmission function, Thermal modeling, Thermography, Fluctuations and noise, Image sensors, Image processing, Physics, Solid modeling, 3D modeling

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Proceedings Article | 27 August 2008 Paper

Micro-scale modeling of contaminant effects on surface optical properties

M. Gartley, J. Schott, S. Brown

Proceedings Volume 7086, 70860H (2008) https://doi.org/10.1117/12.796428

KEYWORDS: Bidirectional reflectance transmission function, Soil contamination, Reflectivity, Polarization, Particles, Scattering, Reflection, Sensors, Water, 3D modeling

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Proceedings Article | 11 April 2008 Paper

Apparent temperature dependence on localized atmospheric water vapor

Matthew Montanaro, Carl Salvaggio, Scott Brown, David Messinger, Alfred Garrett

Proceedings Volume 6966, 696618 (2008) https://doi.org/10.1117/12.774694

KEYWORDS: Sensors, Black bodies, Directed energy weapons, Atmospheric sensing, Atmospheric modeling, Infrared radiation, Long wavelength infrared, Atmospheric particles, Thermal modeling, Platinum

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The atmosphere is a critical factor in remote sensing. Radiance from a target must pass through the air column to reach the sensor. The atmosphere alters the radiance reaching the sensor by attenuating the radiance from the target via scattering and absorption and by introducing an upwelling radiance. In the thermal infrared, these effects will introduce errors in the derived apparent temperature of the target if not properly accounted for. The temperature error is defined as the difference between the target leaving apparent temperature and observed apparent temperature. The effects of the atmosphere must be understood in order to develop methods to compensate for this error. Different atmospheric components will affect the radiation passing through it in different ways. Certain components may be more important than others depending on the remote sensing application. The authors are interested in determining the actual temperature of the superstructure that composes a mechanical draft cooling tower (MDCT), hence water vapor is the primary constituent of concern. The tower generates a localized water vapor plume located between the target and sensor. The MODTRAN radiative transfer code is used to model the effects of a localized exhaust plume from a MDCT in the longwave infrared. The air temperature and dew point depression of the plume and the thickness of the plume are varied to observe the effect on the apparent temperature error. In addition, the general atmospheric conditions are varied between two standard MODTRAN atmospheres to study any effect that ambient conditions have on the apparent temperature error. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling tool is used to simulate the radiance reaching a thermal sensor from a target after passing through the water vapor plume. The DIRSIG results are validated against the MODTRAN results. This study shows that temperature errors of as much as one Kelvin can be attributed to the presence of a localized water vapor plume.

Proceedings Article | 31 March 2008 Paper

Micro-scale surface and contaminate modeling for polarimetric signature prediction

M. Gartley, S. Brown, J. Schott

Proceedings Volume 6972, 697213 (2008) https://doi.org/10.1117/12.801904

KEYWORDS: Polarimetry, Scattering, Bidirectional reflectance transmission function, Reflection, Reflectivity, Refraction, 3D modeling, Radiometry, Sensors, Directed energy weapons

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Proceedings Article | 13 September 2007 Paper

Polarimetric scene modeling in the thermal infrared

M. Gartley, S. Brown, A. Goodenough, N. Sanders, J. Schott

Proceedings Volume 6682, 66820C (2007) https://doi.org/10.1117/12.740528

KEYWORDS: Bidirectional reflectance transmission function, Polarization, Polarimetry, Reflectivity, Thermography, Cameras, Thermal modeling, Infrared imaging, Polarizers, Infrared radiation

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Proceedings Article | 12 September 2007 Paper

Synthetic data generation of high-resolution hyperspectral data using DIRSIG

Marek Jakubowski, David Pogorzala, Timothy Hattenberger, Scott Brown, John Schott

Proceedings Volume 6661, 66610G (2007) https://doi.org/10.1117/12.735264

KEYWORDS: Sensors, Vegetation, Digital imaging, Detection and tracking algorithms, Data modeling, Remote sensing, Reflectivity, Algorithm development, Atmospheric modeling, Calibration

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Proceedings Article | 7 May 2007 Paper

Radiometric modeling of cavernous targets to assist in the determination of absolute temperature for input to process models

Matthew Montanaro, Carl Salvaggio, Scott Brown, David Messinger, Adam Goodenough, Alfred Garrett, Eliel Villa-Aleman

Proceedings Volume 6565, 656511 (2007) https://doi.org/10.1117/12.718666

KEYWORDS: Bidirectional reflectance transmission function, Reflection, Reflectivity, Process modeling, Sensors, Black bodies, Infrared imaging, Solid modeling, Digital imaging, 3D modeling

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Determining the temperature of an internal surface within cavernous targets, such as the interior wall of a mechanical draft cooling tower, from remotely sensed imagery is important for many surveillance applications that provide input to process models. The surface leaving radiance from an observed target is a combination of the self-emitted radiance and the reflected background radiance. The self-emitted radiance component is a function of the temperature-dependent blackbody radiation and the view-dependent directional emissivity. The reflected background radiance component depends on the bidirectional reflectance distribution function (BRDF) of the surface, the incident radiance from surrounding sources, and the BRDF for each of these background sources. Inside a cavity, the background radiance emanating from any of the multiple internal surfaces will be a combination of the self-emitted and reflected energy from the other internal surfaces as well as the downwelling sky radiance. This scenario provides for a complex radiometric inversion problem in order to arrive at the absolute temperature of any of these internal surfaces. The cavernous target has often been assumed to be a blackbody, but in field experiments it has been determined that this assumption does not always provide an accurate surface temperature. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) modeling tool is being used to represent a cavity target. The model demonstrates the dependence of the radiance reaching the sensor on the emissivity of the internal surfaces and the multiple internal interactions between all the surfaces that make up the overall target. The cavity model is extended to a detailed model of a mechanical draft cooling tower. The predictions of derived temperature from this model are compared to those derived from actual infrared imagery collected with a helicopter-based broadband infrared imaging system collected over an operating tower located at the Savannah River National Laboratory site.

Proceedings Article | 7 May 2007 Paper

Recreation of a nominal polarimetric scene using synthetic modeling tools

David Pogorzala, Scott Brown, David Messinger, Chabitha Devaraj

Proceedings Volume 6565, 65650Z (2007) https://doi.org/10.1117/12.721207

KEYWORDS: Data modeling, Polarimetry, Polarization, Bidirectional reflectance transmission function, Sensors, Atmospheric modeling, Reflectivity, RGB color model, Imaging systems, Electro optical modeling

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Proceedings Article | 7 May 2007 Paper

A framework for polarized radiance signature prediction for natural scenes

Chabitha Devaraj, Scott Brown, David Messinger, Adam Goodenough, David Pogorzala

Proceedings Volume 6565, 65650Y (2007) https://doi.org/10.1117/12.719798

KEYWORDS: Polarization, Sun, Scattering, Cameras, Bidirectional reflectance transmission function, Rayleigh scattering, Atmospheric modeling, Electro optical modeling, Sensors, Reflection

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Proceedings Article | 19 May 2006 Paper

Semi-automated DIRSIG scene modeling from 3D LIDAR and passive imaging sources

S. Lach, S. Brown, J. Kerekes

Proceedings Volume 6214, 62140I (2006) https://doi.org/10.1117/12.666096

KEYWORDS: LIDAR, Sensors, Data modeling, Image processing, Image fusion, Digital filtering, 3D modeling, 3D image processing, Digital imaging, Image registration

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Proceedings Article | 19 May 2006 Paper

First-principles based LIDAR simulation environment for scenes with participating mediums

Daniel Blevins, Scott Brown, John Schott

Proceedings Volume 6214, 62140G (2006) https://doi.org/10.1117/12.665958

KEYWORDS: Scattering, LIDAR, Sensors, Atmospheric modeling, Absorption, Laser scattering, Systems modeling, Time correlated photon counting, Statistical analysis, Multiple scattering

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Proceedings Article | 12 May 2006 Paper

A flexible hyperspectral simulation tool for complex littoral environments

Adam Goodenough, Rolando Raqueño, Michael Bellandi, Scott Brown, John Schott

Proceedings Volume 6204, 62040F (2006) https://doi.org/10.1117/12.665827

KEYWORDS: Monte Carlo methods, Sensors, Scattering, Photon transport, Optical spheres, Submerged target modeling, Atmospheric propagation, Hyperspectral simulation, Optical properties, Data modeling

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Proceedings Article | 1 June 2005 Paper

Generation of a combined dataset of simulated radar and EO/IR imagery

N. Baccheschi, S. Brown, J. Kerekes, J. Schott

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.605711

KEYWORDS: Reflectors, Data modeling, Radar, Sensors, Computer aided design, Synthetic aperture radar, Atmospheric modeling, Solid modeling, Interferometric synthetic aperture radar, Electro optical modeling

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Proceedings Article | 19 May 2005 Paper

Time-gated topographic LIDAR scene simulation

Scott Brown, Daniel Blevins, John Schott

Proceedings Volume 5791, (2005) https://doi.org/10.1117/12.604326

KEYWORDS: Sensors, LIDAR, Systems modeling, Data modeling, Atmospheric modeling, Time correlated photon counting, Scattering, Photon counting, 3D modeling, RGB color model

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Proceedings Article | 15 October 2004 Paper

Surface and buried landmine scene generation and validation using the digital imaging and remote sensing image generation model

Erin Peterson, Scott Brown, Timothy Hattenberger, John Schott

Proceedings Volume 5546, (2004) https://doi.org/10.1117/12.561264

KEYWORDS: Land mines, Mining, Data modeling, Algorithm development, Detection and tracking algorithms, Electro optical modeling, Digital imaging, Statistical analysis, Long wavelength infrared, Remote sensing

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Proceedings Article | 15 October 2004 Paper

High-resolution slant-angle scene generation and validation of concealed targets in DIRSIG

Kris Barcomb, John Schott, Scott Brown, Timothy Hattenberger

Proceedings Volume 5546, (2004) https://doi.org/10.1117/12.561256

KEYWORDS: Detection and tracking algorithms, Sensors, Algorithm development, Target detection, Camouflage, Automatic target recognition, Image classification, Data modeling, Image resolution, 3D modeling

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Proceedings Article | 7 January 2004 Paper

Performance analysis of improved methodology for incorporation of spatial/spectral variability in synthetic hyperspectral imagery

Neil Scanlan, John Schott, Scott Brown

Proceedings Volume 5159, (2004) https://doi.org/10.1117/12.509858

KEYWORDS: Performance modeling, Fermium, Frequency modulation, Particle filters, Statistical modeling, Data modeling, Visual process modeling, Algorithm development, Volume rendering, Digital imaging

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Synthetic imagery has traditionally been used to support sensor design by enabling design engineers to pre-evaluate image products during the design and development stages. Increasingly exploitation analysts are looking to synthetic imagery as a way to develop and test exploitation algorithms before image data are available from new sensors. Even when sensors are available, synthetic imagery can significantly aid in algorithm development by providing a wide range of “ground truthed” images with varying illumination, atmospheric, viewing and scene conditions. One limitation of synthetic data is that the background variability is often too bland. It does not exhibit the spatial and spectral variability present in real data. In this work, four fundamentally different texture modeling algorithms will first be implemented as necessary into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model environment. Two of the models to be tested are variants of a statistical Z-Score selection model, while the remaining two involve a texture synthesis and a spectral end-member fractional abundance map approach, respectively. A detailed comparative performance analysis of each model will then be carried out on several texturally significant regions of the resultant synthetic hyperspectral imagery. The quantitative assessment of each model will utilize a set of three peformance metrics that have been derived from spatial Gray Level Co-Occurrence Matrix (GLCM) analysis, hyperspectral Signal-to-Clutter Ratio (SCR) measures, and a new concept termed the Spectral Co-Occurrence Matrix (SCM) metric which permits the simultaneous measurement of spatial and spectral texture. Previous research efforts on the validation and performance analysis of texture characterization models have been largely qualitative in nature based on conducting visual inspections of synthetic textures in order to judge the degree of similarity to the original sample texture imagery. The quantitative measures used in this study will in combination attempt to determine which texture characterization models best capture the correct statistical and radiometric attributes of the corresponding real image textures in both the spatial and spectral domains. The motivation for this work is to refine our understanding of the complexities of texture phenomena so that an optimal texture characterization model that can accurately account for these complexities can be eventually implemented into a synthetic image generation (SIG) model. Further, conclusions will be drawn regarding which of the candidate texture models are able to achieve realistic levels of spatial and spectral clutter, thereby permitting more effective and robust testing of hyperspectral algorithms in synthetic imagery.

Proceedings Article | 5 September 2003 Paper

Advances in wide-area hyperspectral image simulation

Emmett Ientilucci, Scott Brown

Proceedings Volume 5075, (2003) https://doi.org/10.1117/12.488706

KEYWORDS: Reflectivity, Detection and tracking algorithms, Databases, Data modeling, Hyperspectral imaging, Hyperspectral simulation, Target detection, Digital imaging, Bidirectional reflectance transmission function, Atmospheric modeling

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Proceedings Article | 8 November 2002 Paper

Incorporation of polarization into the DIRSIG synthetic image generation model

Jason Meyers, John Schott, Scott Brown

Proceedings Volume 4816, (2002) https://doi.org/10.1117/12.451545

KEYWORDS: Bidirectional reflectance transmission function, Polarization, Sensors, Polarimetry, Surface roughness, Refraction, Imaging systems, Copper, Data modeling, Aluminum

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Proceedings Article | 8 November 2002 Paper

Elastic ladar modeling for synthetic imaging applications

Robin Burton, John Schott, Scott Brown

Proceedings Volume 4816, (2002) https://doi.org/10.1117/12.451630

KEYWORDS: Atmospheric modeling, Systems modeling, LIDAR, Speckle, Beam propagation method, Atmospheric propagation, Receivers, Sensors, Laser scattering, Scattering

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The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is a synthetic imagery generation model developed at the Center for Imaging Science (CIS) at the Rochester Institute of Technology (RIT). It is a quantitative first principle based model that calculates the sensor reaching radiance from the visible through to the long wave infrared on a spectral basis. DIRSIG generates a very accurate representation of what a sensor would see by modeling all the processes involved in the imaging chain. Currently, DIRSIG only models passive sources such as the sun and blackbody radiation due to the temperature of an object. Active systems have the benefit of the user being able to control the illumination source and tailor it for specific applications. Remote sensing Laser Detection and Ranging (LADAR) systems that utilize a laser as the active source have been in existence for over 30 years. Recent advances in tunable lasers and infrared detectors have allowed much more sophisticated and accurate work to be done, but a comprehensive spectral LADAR model has yet to be developed. In order to provide a tool to assist in LADAR development, this research incorporates a first principle based elastic LADAR model into DIRSIG. It calculates the irradiance onto the focal plane on a spectral basis for both the atmospheric and topographic return, based on the system characteristics and the assumed atmosphere. The geometrical form factor, a measure of the overlap between the sensor and receiver field-of-view, is carefully accounted for in both the monostatic and bistatic cases. The model includes the effect of multiple bounces from topographical targets. Currently, only direct detection systems will be modeled. Several sources of noise are extensively modeled, such as speckle from rough surfaces. Additionally, atmospheric turbulence effects including scintillation, beam effects, and image effects are accounted for. To allow for future growth, the model and coding are modular and anticipate the inclusion of advanced sensor modules and inelastic scattering.

Proceedings Article | 23 August 2000 Paper

Comprehensive hyperspectral system simulation: I. Integrated sensor scene modeling and the simulation architecture

Bruce Shetler, Daniel Mergens, Chia Chang, Fredrick Mertz, John Schott, Scott Brown, Robert Strunce, Fran Maher, Steve Kubica, Richard de Jonckheere, Bradford Tousley

Proceedings Volume 4049, (2000) https://doi.org/10.1117/12.410379

KEYWORDS: Atmospheric modeling, Systems modeling, Databases, Data modeling, Hyperspectral simulation, Sensors, Reflectivity, Human-machine interfaces, Computer architecture, Computer simulations

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Proceedings Article | 24 July 2000 Paper

Hyperspectral simulation of chemical weapon dispersal patterns using DIRSIG

Peter Arnold, Scott Brown, John Schott

Proceedings Volume 4029, (2000) https://doi.org/10.1117/12.392538

KEYWORDS: Clouds, Algorithm development, 3D modeling, Absorption, Sensors, Data modeling, Atmospheric modeling, Turbulence, Environmental sensing

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Proceedings Article | 24 July 2000 Paper

Characterization techniques for incorporating backgrounds into DIRSIG

Scott Brown, John Schott

Proceedings Volume 4029, (2000) https://doi.org/10.1117/12.392528

KEYWORDS: Reflectivity, Sensors, Databases, Algorithm development, Volume rendering, Tin, Image sensors, Thermal modeling, Data modeling, Detection and tracking algorithms

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The appearance of operation hyperspectral imaging spectrometers in both solar and thermal regions has lead to the development of a variety of spectral detection algorithms. The development and testing of these algorithms requires well characterized field collection campaigns that can be time and cost prohibitive. Radiometrically robust synthetic image generation (SIG) environments that can generate appropriate images under a variety of atmospheric conditions and with a variety of sensors offers an excellent supplement to reduce the scope of the expensive field collections. In addition, SIG image products provide the algorithm developer with per-pixel truth, allowing for improved characterization of the algorithm performance. To meet the needs of the algorithm development community, the image modeling community needs to supply synthetic image products that contain all the spatial and spectral variability present in real world scenes, and that provide the large area coverage typically acquired with actual sensors. This places a heavy burden on synthetic scene builders to construct well characterized scenes that span large areas. Several SIG models have demonstrated the ability to accurately model targets (vehicles, buildings, etc.) Using well constructed target geometry (from CAD packages) and robust thermal and radiometry models. However, background objects (vegetation, infrastructure, etc.) dominate the percentage of real world scene pixels and utilizing target building techniques is time and resource prohibitive. This paper discusses new methods that have been integrated into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to characterize backgrounds. The new suite of scene construct types allows the user to incorporate both terrain and surface properties to obtain wide area coverage. The terrain can be incorporated using a triangular irregular network (TIN) derived from elevation data or digital elevation model (DEM) data from actual sensors, temperature maps, spectral reflectance cubes (possible derived from actual sensors), and/or material and mixture maps. Descriptions and examples of each new technique are presented as well as hybrid methods to demonstrate target embedding in real world imagery.

Proceedings Article | 24 July 2000 Paper

Utilization of DIRSIG in support of real-time infrared scene generation

Jeffrey Sanders, Scott Brown

Proceedings Volume 4029, (2000) https://doi.org/10.1117/12.392535

KEYWORDS: Computer simulations, Data modeling, Thermal modeling, Atmospheric modeling, Sensors, Infrared radiation, Thermography, Reflectivity, Infrared imaging, Physics

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Real-time infrared scene generation for hardware-in-the-loop has been a traditionally difficult challenge. Infrared scenes are usually generated using commercial hardware that was not designed to properly handle the thermal and environmental physics involved. Real-time infrared scenes typically lack details that are included in scenes rendered in no-real- time by ray-tracing programs such as the Digital Imaging and Remote Sensing Scene Generation (DIRSIG) program. However, executing DIRSIG in real-time while retaining all the physics is beyond current computational capabilities for many applications. DIRSIG is a first principles-based synthetic image generation model that produces multi- or hyper-spectral images in the 0.3 to 20 micron region of the electromagnetic spectrum. The DIRSIG model is an integrated collection of independent first principles based on sub-models, each of which works in conjunction to produce radiance field images with high radiometric fidelity. DIRSIG uses the MODTRAN radiation propagation model for exo-atmospheric irradiance, emitted and scattered radiances (upwelled and downwelled) and path transmission predictions. This radiometry submodel utilizes bidirectional reflectance data, accounts for specular and diffuse background contributions, and features path length dependent extinction and emission for transmissive bodies (plumes, clouds, etc.) which may be present in any target, background or solar path. This detailed environmental modeling greatly enhances the number of rendered features and hence, the fidelity of a rendered scene. While DIRSIG itself cannot currently be executed in real-time, its outputs can be used to provide scene inputs for real-time scene generators. These inputs can incorporate significant features such as target to background thermal interactions, static background object thermal shadowing, and partially transmissive countermeasures. All of these features represent significant improvements over the current state of the art in real-time IR scene generation.

Proceedings Article | 29 January 1999 Paper

Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development

John Schott, Scott Brown, Rolando Raqueno, Harry Gross, Gary Robinson

Proceedings Volume 3584, (1999) https://doi.org/10.1117/12.339823

KEYWORDS: Image fusion, Algorithm development, Sensors, Image processing, Reflectivity, Thermal modeling, Digital imaging, Atmospheric modeling, Spectral resolution, Data modeling

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Proceedings Article | 18 November 1998 Paper

Multispectral simulation environment for modeling low-light-level sensor systems

Emmett Ientilucci, Scott Brown, John Schott, Rolando Raqueno

Proceedings Volume 3434, (1998) https://doi.org/10.1117/12.331229

KEYWORDS: Image fusion, Sensors, Image processing, Charge-coupled devices, Systems modeling, Image sensors, Photons, Algorithm development, Image intensifiers, Modulation transfer functions

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Image intensifying cameras have been found to be extremely useful in low-light-level (LLL) scenarios including military night vision and civilian rescue operations. These sensors utilize the available visible region photons and an amplification process to produce high contrast imagery. It has been demonstrated that processing techniques can further enhance the quality of this imagery. For example, fusion with matching thermal IR imagery can improve image content when very little visible region contrast is available. To aid in the improvement of current algorithms and the development of new ones, a high fidelity simulation environment capable of producing radiometrically correct multi-band imagery for low- light-level conditions is desired. This paper describes a modeling environment attempting to meet these criteria by addressing the task as two individual components: (1) prediction of a low-light-level radiance field from an arbitrary scene, and (2) simulation of the output from a low- light-level sensor for a given radiance field. The radiance prediction engine utilized in this environment is the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model which is a first principles based multi-spectral synthetic image generation model capable of producing an arbitrary number of bands in the 0.28 to 20 micrometer region. The DIRSIG model is utilized to produce high spatial and spectral resolution radiance field images. These images are then processed by a user configurable multi-stage low-light-level sensor model that applies the appropriate noise and modulation transfer function (MTF) at each stage in the image processing chain. This includes the ability to reproduce common intensifying sensor artifacts such as saturation and 'blooming.' Additionally, co-registered imagery in other spectral bands may be simultaneously generated for testing fusion and exploitation algorithms. This paper discusses specific aspects of the DIRSIG radiance prediction for low- light-level conditions including the incorporation of natural and man-made sources which emphasizes the importance of accurate BRDF. A description of the implementation of each stage in the image processing and capture chain for the LLL model is also presented. Finally, simulated images are presented and qualitatively compared to lab acquired imagery from a commercial system.

Proceedings Article | 7 July 1998 Paper

Critical image formation parameters in thermal hyperspectral image simulations

Scott Brown, John Schott, Rolando Raqueno

Proceedings Volume 3375, (1998) https://doi.org/10.1117/12.327164

KEYWORDS: Atmospheric modeling, Algorithm development, Reflectivity, Spectral resolution, Hyperspectral imaging, Thermal modeling, Sensors, Data modeling, Hyperspectral simulation, Image acquisition

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Proceedings Article | 31 October 1997 Paper

Prediction of observed image spectra using synthetic image generation models

John Schott, Shiao Kuo, Scott Brown, Rolando Raqueno

Proceedings Volume 3118, (1997) https://doi.org/10.1117/12.283821

KEYWORDS: Sensors, Data modeling, Atmospheric modeling, 3D modeling, Thermal modeling, Infrared signatures, Image sensors, Reflectivity, Imaging systems, Infrared imaging

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Proceedings Article | 14 October 1996 Paper

Incorporation of transmissive scene element modeling in multispectral image simulation tools

Rolando Raqueno, Scott Brown, John Schott

Proceedings Volume 2828, (1996) https://doi.org/10.1117/12.254186

KEYWORDS: Sensors, Atmospheric modeling, Clouds, Scene simulation, Atmospheric sensing, Imaging systems, Systems modeling, Infrared imaging, Thermography, Infrared radiation

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Proceedings Article | 2 June 1995 Paper

Incorporation of texture in multispectral synthetic image generation tools

John Schott, Carl Salvaggio, Scott Brown, R. Rose

Proceedings Volume 2469, (1995) https://doi.org/10.1117/12.210590

KEYWORDS: Reflectivity, Atmospheric modeling, Digital imaging, Sensors, Multispectral imaging, Target detection, Remote sensing, Environmental sensing, Image segmentation, Image classification

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Proceedings Article | 29 July 1994 Paper

Hybrid optical system for frequency-domain texture classification of imagery

Roger Easton, Marc Comeau, Scott Brown, David Ehrhard

Proceedings Volume 2234, (1994) https://doi.org/10.1117/12.181024

KEYWORDS: Image filtering, Image segmentation, Bandpass filters, Optical filters, LCDs, Fourier transforms, Optical correlators, Video, Linear filtering, Image processing algorithms and systems

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Showing 5 of 49 publications

Conference Committee Involvement (4)

Targets and Backgrounds XII: Characterization and Representation

17 April 2006 | Orlando (Kissimmee), Florida, United States

Targets and Backgrounds XI: Characterization and Representation

28 March 2005 | Orlando, Florida, United States

Targets and Backgrounds X: Characterization and Representation

12 April 2004 | Orlando, Florida, United States

Targets and Backgrounds IX: Characterization and Representation

21 April 2003 | Orlando, Florida, United States

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