Dr. Michael G. Gartley Profile

Dr. Michael G. Gartley

Research Professor at Rochester Institute of Technology

SPIE Involvement:

Conference Program Committee | Author

Publications (35)

Proceedings Article | 30 September 2022 Presentation + Paper

Temporally Adjusted Atmospheric Compensation (TAAC) for space-based multispectral imagery

Michael Gartley, Emmett Ientilucci, James Albano, Sarvani Bhamidi

Proceedings Volume 12232, 122320K (2022) https://doi.org/10.1117/12.2646380

KEYWORDS: Reflectivity, Sensors, Atmospheric modeling, Aerosols, Vegetation, Image processing, Earth observing sensors, Calibration, Short wave infrared radiation

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Proceedings Article | 3 June 2022 Paper

Polarimetric image rendering capability of DIRSIG4

M. Gartley, J. Dank

Proceedings Volume 12112, 121120M (2022) https://doi.org/10.1117/12.2626034

KEYWORDS: Polarimetry, Polarization, Bidirectional reflectance transmission function, Digital imaging, Data modeling, Sensors, Scattering, Passive remote sensing

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Proceedings Article | 8 May 2018 Paper

A wide dense architecture neural network for material classification based on spectral BTF

Gefei Yang, Michael Gartley

Proceedings Volume 10644, 106441R (2018) https://doi.org/10.1117/12.2302911

KEYWORDS: Neural networks, Data modeling, Feature extraction, Image classification, Convolution, Network architectures, Reflectivity, Light sources and illumination, Performance modeling, Bidirectional reflectance transmission function

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Proceedings Article | 5 May 2017 Presentation + Paper

Application of a neural network for reflectance spectrum classification

Gefei Yang, Michael Gartley

Proceedings Volume 10198, 1019817 (2017) https://doi.org/10.1117/12.2261847

KEYWORDS: Neural networks, Reflectivity, Bidirectional reflectance transmission function, Machine learning, Convolution, Image analysis, Light sources and illumination, Cameras, Feature extraction, Image classification, Databases, Data modeling, Image processing, Computer simulations

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Proceedings Article | 1 May 2017 Presentation + Paper

Efficient generation of image chips for training deep learning algorithms

Sanghui Han, Alex Fafard, John Kerekes, Michael Gartley, Emmett Ientilucci, Andreas Savakis, Charles Law, Jason Parhan, Matt Turek, Keith Fieldhouse, Todd Rovito

Proceedings Volume 10202, 1020203 (2017) https://doi.org/10.1117/12.2261702

KEYWORDS: Sensors, Atmospheric modeling, Digital imaging, RGB color model, Roads, Detection and tracking algorithms, Image sensors, Atmospheric sensing, Atmospheric physics, Buildings

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Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with the simulated images, especially when obtaining sufficient real data was particularly challenging.

Proceedings Article | 21 May 2015 Paper

Empirical measurement and model validation of infrared spectra of contaminated surfaces

Sean Archer, Michael Gartley, John Kerekes, Bogdon Cosofret, Jay Giblin

Proceedings Volume 9472, 947215 (2015) https://doi.org/10.1117/12.2177683

KEYWORDS: Aluminum, Bidirectional reflectance transmission function, Contamination, Statistical modeling, Sensors, Data modeling, Temperature metrology, Infrared radiation, Thermal modeling, Digital imaging

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Proceedings Article | 13 June 2014 Paper

A comparison of real and simulated airborne multisensor imagery

Kevin Bloechl, Chris De Angelis, Michael Gartley, John Kerekes, C. Eric Nance

Proceedings Volume 9088, 90880G (2014) https://doi.org/10.1117/12.2050522

KEYWORDS: Sensors, Atmospheric modeling, Reflectivity, Target detection, Signal to noise ratio, RGB color model, Image sensors, Scene simulation, Point spread functions, Image classification

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Proceedings Article | 21 May 2014 Paper

Activity-based intelligence tipping and cueing using polarimetric sensors

Christian Lewis, David Messinger, Michael Gartley

Proceedings Volume 9099, 90990C (2014) https://doi.org/10.1117/12.2050743

KEYWORDS: Sensors, Polarimetry, Video, Polarization, Detection and tracking algorithms, Intelligent sensors, Light emitting diodes, Diodes, RGB color model, Video processing

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Proceedings Article | 21 May 2014 Paper

Polarization in a snap: imaging polarimetry with micropolarizer arrays

Dmitry Vorobiev, Zoran Ninkov, Michael Gartley

Proceedings Volume 9099, 909904 (2014) https://doi.org/10.1117/12.2053164

KEYWORDS: Polarization, Polarizers, Polarimetry, Calibration, Sensors, 3D modeling, Diffraction, Aluminum, Finite-difference time-domain method, Performance modeling

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Proceedings Article | 27 September 2013 Paper

Influence of polarization phenomenology on material discriminability using multi-view polarimetric imagery

Chabitha Devaraj, Michael Gartley, John Schott

Proceedings Volume 8873, 88730C (2013) https://doi.org/10.1117/12.2022605

KEYWORDS: Polarimetry, Polarization, Sensors, Target detection, Imaging systems, Scattering, Image sensors, Remote sensing, Atmospheric sensing, Detection and tracking algorithms

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Proceedings Article | 23 September 2013 Paper

Spatial heterodyne spectrometer: modeling and interferogram processing for calibrated spectral radiance measurements

Cara Perkins, John Kerekes, Michael Gartley

Proceedings Volume 8870, 88700R (2013) https://doi.org/10.1117/12.2023765

KEYWORDS: Long wavelength infrared, Fourier transforms, Calibration, Sensors, Spectral resolution, Signal to noise ratio, Spectroscopy, Heterodyning, Spectral calibration, Data modeling

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Proceedings Article | 11 June 2012 Paper

Evaluation of the suitability of polarimetric scattering and emissivity models with scene generation software

Michael Gartley

Proceedings Volume 8364, 83640P (2012) https://doi.org/10.1117/12.919316

KEYWORDS: Bidirectional reflectance transmission function, Polarimetry, Data modeling, Scattering, Reflectivity, Polarization, Thermal modeling, Optical spheres, Optical properties, Radiative transfer

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Proceedings Article | 8 June 2012 Paper

Relation between degree of polarization and Pauli color coded image to characterize scattering mechanisms

Sanjit Maitra, Michael Gartley, John Kerekes

Proceedings Volume 8364, 83640F (2012) https://doi.org/10.1117/12.918486

KEYWORDS: Polarimetry, Scattering, Synthetic aperture radar, Polarization, Radar, RGB color model, Polarizers, Image fusion, Remote sensing, Sensors

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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 | 24 May 2012 Paper

Evaluation of nighttime imaging limits of visible near-infrared Earth observation platforms

M. Gartley, A. Gerace

Proceedings Volume 8390, 83902D (2012) https://doi.org/10.1117/12.919359

KEYWORDS: Sensors, Alternate lighting of surfaces, Imaging systems, Landsat, Atmospheric modeling, Quantum efficiency, Signal to noise ratio, Water, 3D modeling, Earth observing sensors

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Proceedings Article | 3 May 2012 Paper

Using GIS databases for simulated nightlight imagery

Joshua Zollweg, Michael Gartley, John Roskovensky, Jeffery Mercier

Proceedings Volume 8396, 83960C (2012) https://doi.org/10.1117/12.919510

KEYWORDS: Databases, Buildings, Geographic information systems, Scene simulation, Roads, Light sources, Imaging systems, Data modeling, MATLAB, Digital imaging

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Proposed is a new technique for simulating nighttime scenes with realistically-modelled urban radiance. While nightlight imagery is commonly used to measure urban sprawl,1 it is uncommon to use urbanization as metric to develop synthetic nighttime scenes. In the developed methodology, the open-source Open Street Map (OSM) Geographic Information System (GIS) database is used. The database is comprised of many nodes, which are used to dene the position of dierent types of streets, buildings, and other features. These nodes are the driver used to model urban nightlights, given several assumptions. The rst assumption is that the spatial distribution of nodes is closely related to the spatial distribution of nightlights. Work by Roychowdhury et al has demonstrated the relationship between urban lights and development. 2 So, the real assumption being made is that the density of nodes corresponds to development, which is reasonable. Secondly, the local density of nodes must relate directly to the upwelled radiance within the given locality. Testing these assumptions using Albuquerque and Indianapolis as example cities revealed that dierent types of nodes produce more realistic results than others. Residential street nodes oered the best performance for any single node type, among the types tested in this investigation. Other node types, however, still provide useful supplementary data. Using streets and buildings dened in the OSM database allowed automated generation of simulated nighttime scenes of Albuquerque and Indianapolis in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. The simulation was compared to real data from the recently deployed National Polar-orbiting Operational Environmental Satellite System(NPOESS) Visible Infrared Imager Radiometer Suite (VIIRS) platform. As a result of the comparison, correction functions were used to correct for discrepancies between simulated and observed radiance. Future work will include investigating more advanced approaches for mapping the spatial extent of nightlights, based on the distribution of dierent node types in local neighbourhoods. This will allow the spectral prole of each region to be dynamically adjusted, in addition to simply modifying the magnitude of a single source type.

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 | 7 September 2011 Paper

Enhanced DIRSIG scene simulation by incorporating process models

Jiangqin Sun, David Messinger, Michael Gartley

Proceedings Volume 8158, 81580F (2011) https://doi.org/10.1117/12.893956

KEYWORDS: Process modeling, Scene simulation, Digital imaging, Remote sensing, Sensors, Spectroscopy, Current controlled current source, Hyperspectral imaging, Image sensors, Thermodynamics

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Proceedings Article | 3 June 2011 Paper

Modified bilateral filter for illumination equalization

Samuel Brisebois, M. Gartley

Proceedings Volume 8056, 805604 (2011) https://doi.org/10.1117/12.883549

KEYWORDS: Image filtering, Optical filters, Transform theory, RGB color model, Calibration, Reflectivity, Cameras, Black bodies, Sun, Image quality

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Proceedings Article | 20 May 2011 Paper

Data-driven simulations of the Landsat Data Continuity Mission (LDCM) platform

Aaron Gerace, Mike Gartley, John Schott, Nina Raqueño, Rolando Raqueño

Proceedings Volume 8048, 804815 (2011) https://doi.org/10.1117/12.885561

KEYWORDS: Landsat, Sensors, Data modeling, Space operations, Infrared sensors, Digital imaging, Bridges, Data archive systems, Imaging systems, Earth observing sensors

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The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are two new sensors being developed by the Landsat Data Continuity Mission (LDCM) that will extend over 35 years of archived Landsat data. In a departure from the whiskbroom design used by all previous generations of Landsat, the LDCM system will employ a pushbroom technology. Although the newly adopted modular array, pushbroom architecture has several advantages over the previous whiskbroom design, registration of the multi-spectral data products is a concern. In this paper, the Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to simulate an LDCM collection, which gives the team access to data that would not otherwise be available prior to launch. The DIRSIG model was used to simulate the two-instrument LDCM payload in order to study the geometric and radiometric impacts of the sensor design on the proposed processing chain. The Lake Tahoe area located in eastern California was chosen for this work because of its dramatic change in elevation, which was ideal for studying the geometric effects of the new Landsat sensor design. Multi-modal datasets were used to create the Lake Tahoe site model for use in DIRSIG. National Elevation Dataset (NED) data were used to create the digital elevation map (DEM) required by DIRSIG, QuickBird data were used to identify different material classes in the scene, and ASTER and Hyperion spectral data were used to assign radiometric properties to those classes. In order to model a realistic Landsat orbit in these simulations, orbital parameters were obtained from a Landsat 7 two-line element set and propagated with the SGP4 orbital position model. Line-of-sight vectors defining how the individual detector elements of the OLI and TIRS instruments project through the optics were measured and provided by NASA. Additionally, the relative spectral response functions for the 9 bands of OLI and the 2 bands of TIRS were measured and provided by NASA. The instruments were offset on the virtual satellite and data recorders used to generate ephemeris data for downstream processing. Finally, potential platform jitter spectra were measured and provided by NASA and incorporated into the simulations. Simulated imagery generated by the model was incrementally provided to the rest of the LDCM team in a spiral development cycle to constantly refine the simulations.

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.

SPIE Journal Paper | 1 November 2010

Spectro-polarimetric bidirectional reflectance distribution function determination of in-scene materials and its use in target detection applications

Brent Bartlett, Michael Gartley, David Messinger, Carl Salvaggio, John Schott

JARS, Vol. 4, Issue 01, 043552, (November 2010) https://doi.org/10.1117/12.10.1117/1.3518394

KEYWORDS: Polarimetry, Reflectivity, Target detection, Bidirectional reflectance transmission function, Calibration, RGB color model, Stereolithography, Data modeling, Sensors, Hyperspectral target detection

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SPIE Journal Paper | 1 November 2010

Assessing the impact of spectral and polarimetric data fusion via simulation to support multimodal sensor system design requirements

Brian Flusche, Michael Gartley, John Schott

JARS, Vol. 4, Issue 01, 043562, (November 2010) https://doi.org/10.1117/12.10.1117/1.3525590

KEYWORDS: Polarimetry, Sensors, Detection and tracking algorithms, Signal to noise ratio, Target detection, Data fusion, Sun, Image analysis, Digital imaging, Image sensors

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SPIE Journal Paper | 1 October 2010

Defining a process to fuse polarimetric and spectral data for target detection and explore the trade space via simulation

Brian Flusche, Michael Gartley, John Schott

JARS, Vol. 4, Issue 01, 043550, (October 2010) https://doi.org/10.1117/12.10.1117/1.3516616

KEYWORDS: Polarimetry, Detection and tracking algorithms, Sensors, Target detection, Image fusion, Data fusion, Remote sensing, Image analysis, Atmospheric modeling, Image processing

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Proceedings Article | 13 August 2010 Paper

Exploiting spectral and polarimetric data fusion to enhance target detection performance

Brian Flusche, Michael Gartley, John Schott

Proceedings Volume 7812, 78120C (2010) https://doi.org/10.1117/12.860640

KEYWORDS: Polarimetry, Sensors, Detection and tracking algorithms, Target detection, Image sensors, Data fusion, Image analysis, Image fusion, Atmospheric modeling, Signal to noise ratio

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Proceedings Article | 13 May 2010 Paper

Characterization of material reflectance variation through measurement and simulation

John Kerekes, Caitlin Hart, Michael Gartley, Brent Bartlett, C. Eric Nance

Proceedings Volume 7695, 76950P (2010) https://doi.org/10.1117/12.852211

KEYWORDS: Bidirectional reflectance transmission function, Reflectivity, Statistical modeling, Photons, Polarization, Dental caries, Contamination, Monte Carlo methods, Atmospheric modeling, Cameras

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Proceedings Article | 26 April 2010 Paper

Polarimetric effects in nonpolarimetric imaging

Russel Kauffman, Michael Gartley

Proceedings Volume 7672, 76720N (2010) https://doi.org/10.1117/12.849545

KEYWORDS: Polarimetry, Sensors, Bidirectional reflectance transmission function, Polarization, Radiative transfer, Reflectivity, Solar radiation models, Radiometry, Imaging systems, Remote sensing

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Proceedings Article | 24 April 2010 Paper

Pose estimation of unresolved targets using polarimetric imaging

Michael Gartley, Peter Erbach, Larry Pezzaniti

Proceedings Volume 7672, 767204 (2010) https://doi.org/10.1117/12.851506

KEYWORDS: Polarimetry, Visualization, Radiometry, Polarization, Long wavelength infrared, Cameras, Data modeling, Target acquisition, Reflection, Remote sensing

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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 | 12 August 2009 Paper

Spectro-polarimetric BRDF determination of objects using in-scene calibration materials for polarimetric imagers

Brent Bartlett, Chabitha Devaraj, Michael Gartley, Carl Salvaggio, John Schott

Proceedings Volume 7461, 74610T (2009) https://doi.org/10.1117/12.825359

KEYWORDS: Polarimetry, Calibration, Bidirectional reflectance transmission function, Reflectivity, Imaging systems, Atmospheric modeling, RGB color model, Sensors, Cameras, Polarization

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Proceedings Article | 27 April 2009 Paper

Impact of BRDF on physics-based modeling as applied to target detection in hyperspectral imagery

Emmett Ientilucci, Michael Gartley

Proceedings Volume 7334, 73340T (2009) https://doi.org/10.1117/12.819332

KEYWORDS: Bidirectional reflectance transmission function, Reflectivity, Sensors, Atmospheric modeling, Target detection, Hyperspectral target detection, Atmospheric physics, Hyperspectral imaging, Physics, Detection and tracking algorithms

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Proceedings Article | 27 April 2009 Paper

Topological anomaly detection performance with multispectral polarimetric imagery

M. Gartley, W, Basener

Proceedings Volume 7334, 73341O (2009) https://doi.org/10.1117/12.817843

KEYWORDS: Polarimetry, Detection and tracking algorithms, Target detection, Modulation transfer functions, Signal to noise ratio, Polarization, Multispectral imaging, Computer simulations, Sun, Missiles

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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 | 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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