Dr. Christoph C. Borel-Donohue Profile

Dr. Christoph C. Borel-Donohue

Remote Sensing Scientist/Electronics Engineer at US Army Research Laboratory

SPIE Involvement:

Senior status | Conference Program Committee | Author

Area of Expertise:

Hyper-spectral and multi-spectral imaging , Thermal remote sensing , Microwave remote sensing , Image Processing , Scene simulation , Computer Graphics

Websites:

LinkedIn Website

Profile Summary

Over 20 years of experience in optical and microwave remote sensing with over 80 publications.
Developed temperature emissivity retrieval method ARTEMISS for LWIR.
Developed atmospheric correction algorithms APDA for water vapor retrieval in the NIR.
Performed end-to-end processing for thermal data for a multispectral sensor to retrieve water temperature.

Clearances: TS/SCI

Publications (50)

PROCEEDINGS ARTICLE | April 27, 2018

A real-time object detection framework for aerial imagery using deep neural networks and synthetic training images

Proc. SPIE. 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII

KEYWORDS: Unmanned aerial vehicles, Video, Video surveillance, Surveillance, Computer vision technology, Machine vision, Object recognition, Neural networks, Airborne remote sensing, Situational awareness sensors

PROCEEDINGS ARTICLE | May 23, 2017

Spectral and spatial variability of undisturbed and disturbed grass under different view and illumination directions

Proc. SPIE. 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII

KEYWORDS: Near infrared, Visible radiation, 3D image reconstruction, Reflection, Cameras, Spectroscopy, Reflectivity, Infrared spectroscopy, Reconstruction algorithms, Spatial resolution

PROCEEDINGS ARTICLE | May 3, 2017

Visualizing UAS-collected imagery using augmented reality

Proc. SPIE. 10207, Next-Generation Analyst V

KEYWORDS: 3D applications, Visualization, Cameras, Clouds, 3D modeling, Photogrammetry, Augmented reality, Software development, Head-mounted displays, Defense technologies, 3D displays, 3D vision, 3D visualizations, 3D image processing, Situational awareness sensors

PROCEEDINGS ARTICLE | May 17, 2016

Solid target spectral variability in LWIR

Proc. SPIE. 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII

KEYWORDS: Target detection, Long wavelength infrared, Data modeling, Sensors, Calibration, Pattern recognition, Remote sensing, Solids, Black bodies, Spectral resolution

PROCEEDINGS ARTICLE | May 21, 2015

Pattern recognition in hyperspectral persistent imaging

Proc. SPIE. 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI

KEYWORDS: Long wavelength infrared, Hyperspectral imaging, Sensors, Calibration, Pattern recognition, Clouds, Data acquisition, Black bodies, Spectral resolution, Atmospheric modeling

PROCEEDINGS ARTICLE | June 19, 2014

Statistical moments based methods for detecting sub-pixel target tracks in large image sequences

Proc. SPIE. 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II

KEYWORDS: Target detection, Signal to noise ratio, Point spread functions, Detection and tracking algorithms, Imaging systems, Sensors, Image processing, Interference (communication), Distributed computing, Palladium

PROCEEDINGS ARTICLE | June 13, 2014

First observations using SPICE hyperspectral dataset

Proc. SPIE. 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX

KEYWORDS: Long wavelength infrared, Sensors, Calibration, Remote sensing, Clouds, Data acquisition, Black bodies, Spectral resolution, Atmospheric sensing, Atmospheric modeling

PROCEEDINGS ARTICLE | May 23, 2013

Extraction of human gait signatures: an inverse kinematic approach using Groebner basis theory applied to gait cycle analysis

Proc. SPIE. 8734, Active and Passive Signatures IV

KEYWORDS: Human-machine interfaces, MATLAB, Data modeling, Pattern recognition, Kinematics, Analytical research, Molybdenum, Motion models, Motion analysis, Gait analysis

PROCEEDINGS ARTICLE | October 15, 2012

Range-invariant anomaly detection applied to imaging Fourier transform spectrometry data

Proc. SPIE. 8515, Imaging Spectrometry XVII

KEYWORDS: Detection and tracking algorithms, Sensors, Calibration, Image processing, Spectroscopy, Fourier transforms, Data processing, Black bodies, Niobium, Algorithm development

PROCEEDINGS ARTICLE | October 7, 2011

Multispectral vegetative canopy parameter retrieval

Proc. SPIE. 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII

KEYWORDS: Near infrared, Agriculture, Data modeling, Scattering, Sensors, Nitrogen, Reflectivity, Vegetation, Atmospheric modeling, Absorption

PROCEEDINGS ARTICLE | May 20, 2011

Multi- and hyperspectral scene modeling

Proc. SPIE. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII

KEYWORDS: Near infrared, Scattering, Light scattering, Reflectivity, 3D modeling, Vegetation, Multiple scattering, Ray tracing, 3D image processing, RGB color model

PROCEEDINGS ARTICLE | August 13, 2010

Improved panchromatic sharpening of multi-spectral image data

Proc. SPIE. 7812, Imaging Spectrometry XV

KEYWORDS: Image fusion, Near infrared, Point spread functions, Image restoration, Image resolution, Vegetation, Multispectral imaging, Image quality, Radiometry, RGB color model

PROCEEDINGS ARTICLE | August 13, 2010

First imaging Fourier-transform spectral measurements of detonation in an internal combustion engine

Proc. SPIE. 7812, Imaging Spectrometry XV

KEYWORDS: Infrared imaging, Interferometers, Combustion, Computer simulations, Time metrology, Sapphire, Spectral resolution, Infrared radiation, Reconstruction algorithms, Algorithm development

PROCEEDINGS ARTICLE | August 13, 2010

Improving the detectability of small spectral targets through spatial filtering

Proc. SPIE. 7812, Imaging Spectrometry XV

KEYWORDS: Target detection, Signal to noise ratio, Point spread functions, Detection and tracking algorithms, Spatial filters, Sensors, Image filtering, Dysprosium, Image enhancement, Electronic filtering

PROCEEDINGS ARTICLE | May 13, 2010

Simulating systematic scene-change artifacts in Fourier-transform spectroscopy

Proc. SPIE. 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI

KEYWORDS: Apodization, Interferometers, Spectroscopy, Fourier transforms, Interferometry, Time metrology, Transmittance, Atmospheric modeling, Temperature metrology, Absorption

PROCEEDINGS ARTICLE | April 27, 2009

Algorithm for retrieving vegetative canopy and leaf parameters from multi- and hyperspectral imagery

Proc. SPIE. 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV

KEYWORDS: Hyperspectral imaging, Visual process modeling, Soil science, Curium, Data modeling, Sensors, Reflectivity, Vegetation, Absorption

PROCEEDINGS ARTICLE | August 27, 2008

Method for retrieving column water vapor at night using mid-wave infrared bands

Proc. SPIE. 7086, Imaging Spectrometry XIII

KEYWORDS: Mid-IR, MODIS, Sensors, Computer simulations, Atmospheric physics, Humidity, Terbium, Spatial resolution, Atmospheric modeling, Temperature metrology

PROCEEDINGS ARTICLE | May 7, 2007

Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data

Proc. SPIE. 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII

KEYWORDS: Sensors, Error analysis, Computer simulations, Atmospheric corrections, Spectral calibration, Algorithm development, Ozone, Atmospheric modeling, Temperature metrology, Absorption

In the hyperspectral thermal data analysis temperature-emissivity separation has the same function as reflectance retrieval in the visible and shortwave infrared. The problem however is more complicated since in the thermal the surface emits and reflects radiation. The measured radiance is a function of the materials' surface emissivity and temperature, reflected down welling radiance (clear sky, clouds environment) and the path radiance (temperature and gas (e.g. water vapor, ozone) profiles). The current implementation of the Automatic Retrieval of Temperature and EMIssivity using Spectral Smoothness (ARTEMISS) uses look-up-tables (LUT) to infer the best fitting atmosphere which results in the smallest residual to the In-Scene Atmospheric Compensation (ISAC) estimated transmission. Over last few years we have developed an end-to-end simulation of the hyper spectral exploitation process by generating synthetic data to simulate datasets with "known" ground truth, modeling propagation through the atmosphere, adding sensor effects (telescope, detector, read-out electronics), radiometric and spectral calibration, and test the temperature emissivity separation algorithm. We will present an error analysis where we shifted the band centers, varied the full-width half maximum (FWHM) of the spectral response function, changed the spectral resolution, added noise and varied the atmospheric model. We will also discuss a general method to retrieve the spectral smile as a function of wavelength and the FWHM from hyperspectral data with only approximate spectral calibration. We found that our algorithm has trouble finding a unique solution when the watervapor exceeds about 3 g/cm² and will discuss remedies for this situation. To speedup the LUT generation we have developed fast and robust initial atmospheric parameter estimators (water vapor, ozone, near surface atmospheric layer temperature) based on channel ratios and brightness temperatures in atmospheric absorption regions for the LWIR.

PROCEEDINGS ARTICLE | September 1, 2006

Atmospheric correction of airborne POLDER polarimetric imagery using vectorized 6S

Proc. SPIE. 6302, Imaging Spectrometry XI

KEYWORDS: Optical filters, Sun, Polarization, Scattering, Sensors, Aerosols, Reflectivity, Polarimetry, Atmospheric corrections, Image filtering

We are interested in using wide-field-of-view polarimetric imagers such as airborne POLDER (LOA, University of Lille) and AMPI (NASA) to study polarization signatures of surface targets. The airborne POLDER instrument has a field of view +/- 43° in along-track and +/- 51° cross-track. It records imagery through a rotating filter wheel with spectral filters, and in two spectral bands with linear polarization filters orientated at 0°, 60°, and 120° while flying at 70 m/s, generating images that need to be registered before spectra or the Stokes vectors can be computed. The atmospheric contributions, particularly in the short-wavelength visible bands, are anisotropic due to the scattering from molecules and aerosols, and the contrast is quite low, making automated image registration impossible. Thus, it is necessary to remove the polarized upwelling atmospheric contributions from the at-sensor radiances before the images can be registered, and target-leaving Stokes parameters can be derived. We accomplished the atmospheric correction by using the recently released vectorized version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6Sv) from Eric Vermote et al. 1997. We wrote a front-end in IDL to run 6Sv over a range of viewing zenith and azimuth angles. The resulting Stokes parameters are then interpolated to a grid of input viewing coordinates for the sensor. Next, the Stokes hemispherical path radiances are converted to match the data of the airborne POLDER instrument for the linear polarization angles of 0°, 60°, and 120°. The upwelling atmospheric intensities are subtracted from the respective polarimetric intensity images and the difference is divided by the transmission multiplied with the solar irradiance. This atmospheric correction significantly reduces the low-frequency variations in intensity in the images resulting from atmospheric scattering. The atmospherically corrected intensity images at 0°, 60°, and 120° are then used to calculate the Stokes parameter images in the usual fashion. From the Stokes parameter images we calculate the degree and azimuth of linear polarization images for the surface.

PROCEEDINGS ARTICLE | May 8, 2006

MODTRAN5: 2006 update

Proc. SPIE. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII

KEYWORDS: MODIS, Data modeling, Databases, Multiple scattering, Spectral resolution, Transmittance, Infrared radiation, Analytical research, Atmospheric modeling, Absorption

PROCEEDINGS ARTICLE | June 1, 2005

MODTRAN 5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options: update

Proc. SPIE. 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI

KEYWORDS: Data modeling, Databases, Multiple scattering, Spectral resolution, Transmittance, Analytical research, Statistical modeling, Thermal modeling, Atmospheric modeling, Absorption

PROCEEDINGS ARTICLE | January 7, 2004

LANL MTI science team experience

Proc. SPIE. 5159, Imaging Spectrometry IX

KEYWORDS: Thermography, Data modeling, Aerosols, Calibration, Satellites, Reflectivity, Clouds, Algorithm development, Atmospheric modeling, Model-based design

The Multispectral Thermal Imager (MTI) is a technology test and demonstration satellite whose primary mission involved a finite number of technical objectives. MTI was not designed, or supported, to become a general purpose operational satellite. The role of the MTI science team is to provide a core group of system-expert scientists who perform the scientific development and technical evaluations needed to meet programmatic objectives. Another mission for the team is to develop algorithms to provide atmospheric compensation and quantitative retrieval of surface parameters to a relatively small community of MTI users. Finally, the science team responds and adjusts to unanticipated events in the life of the satellite. Broad or general lessons learned include the value of working closely with the people who perform the calibration of the data as well as those providing archived image and retrieval products. Close interaction between the Los Alamos National Laboratory (LANL) teams was very beneficial to the overall effort as well as the science effort. Secondly, as time goes on we make increasing use of gridded global atmospheric data sets which are products of global weather model data assimilation schemes. The Global Data Assimilation System information is available globally every six hours and the Rapid Update Cycle products are available over much of the North America and its coastal regions every hour. Additionally, we did not anticipate the quantity of validation data or time needed for thorough algorithm validation. Original validation plans called for a small number of intensive validation campaigns soon after launch. One or two intense validation campaigns are needed but are not sufficient to define performance over a range of conditions or for diagnosis of deviations between ground and satellite products. It took more than a year to accumulate a good set of validation data. With regard to the specific programmatic objectives, we feel that we can do a reasonable job on retrieving surface water temperatures well within the 1°C objective under good observing conditions. Before the loss of the onboard calibration system, sea surface retrievals were usually within 0.5°C. After that, the retrievals are usually within 0.8°C during the day and 0.5°C at night. Daytime atmospheric water vapor retrievals have a scatter that was anticipated: within 20%. However, there is error in using the Aerosol Robotic Network retrievals as validation data which may be due to some combination of calibration uncertainties, errors in the ground retrievals, the method of comparison, and incomplete physics. Calibration of top-of-atmosphere radiance measurements to surface reflectance has proven daunting. We are not alone here: it is a difficult problem to solve generally and the main issue is proper compensation for aerosol effects. Getting good reflectance validation data over a number of sites has proven difficult but, when assumptions are met, the algorithm usually performs quite well. Aerosol retrievals for off-nadir views seem to perform better than near-nadir views and the reason for this is under investigation. Land surface temperature retrieval and temperature-emissivity separations are difficult to perform accurately with multispectral sensors. An interactive cloud masking system was implemented for production use. Clouds are so spectrally and spatially variable that users are encouraged to carefully evaluate the delivered mask for their own needs. The same is true for the water mask. This mask is generated from a spectral index that works well for deep, clear water, but there is much variability in water spectral reflectance inland and along coasts. The value of the second-look maneuvers has not yet been fully or systematically evaluated. Early experiences indicated that the original intentions have marginal value for MTI objectives, but potentially important new ideas have been developed. Image registration (the alignment of data from different focal planes) and band-to-band registration has been a difficult problem to solve, at least for mass production of the images in a processing pipeline. The problems, and their solutions, are described in another paper.

PROCEEDINGS ARTICLE | January 17, 2002

Calibration techniques for imaging FTIR data

Proc. SPIE. 4480, Imaging Spectrometry VII

KEYWORDS: Thermography, Signal to noise ratio, Infrared imaging, Mirrors, FT-IR spectroscopy, Sensors, Calibration, Error analysis, Infrared radiation, Lawrencium

PROCEEDINGS ARTICLE | January 17, 2002

Using state-of-the-art hydrodynamic models to generate synthetic data cubes for imaging spectral sensing applications

Proc. SPIE. 4480, Imaging Spectrometry VII

KEYWORDS: Infrared imaging, Data modeling, Sensors, Chemical species, Spectral resolution, Atmospheric sensing, Thermal modeling, Atmospheric modeling, Systems modeling, Spectral models

PROCEEDINGS ARTICLE | August 20, 2001

Multiscale thermal-infrared measurements of the Mauna Loa caldera, Hawaii

Proc. SPIE. 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII

KEYWORDS: Thermography, Infrared imaging, Sensors, Satellites, Satellite imaging, Multispectral imaging, Radiometry, Atmospheric sensing, Earth observing sensors, Temperature metrology

Until recently, most thermal infrared measurements of natural scenes have been made at disparate scales, typically 10^-3-10^-2 (spectra) and 10²-10³m (satellite images), with occasional airborne images (10¹m) filling the gap. Temperature and emissivity fields are spatially heterogeneous over a similar range of scales, depending on scene composition. A common problem for the land surface, therefore, has been relating field spectral and temperature measurements to satellite data, yet in many cases this is necessary if satellite data are to be interpreted to yield meaningful information about the land surface. Recently, three new satellites with thermal imaging capability at the 10¹-10²m scale have been launched: MTI, TERRA, and Landsat 7. MTI acquires multispectral images in the mid-infrared (3-5micrometers ) and longwave infrared (8-10micrometers ) with 20m resolution. ASTER and MODIS aboard TERRA acquire multispectral longwave images at 90m and 500-1000m, respectively, and MODIS also acquires multispectral mid-infrared images. Landsat 7 acquires broadband longwave images at 60m. As part of an experiment to validate the temperature and thermal emissivity values calculated from MTI and ASTER images, we have targeted the summit region of Mauna Loa for field characterization and near-simultaneous satellite imaging, both on daytime and nighttime overpasses, and compare the results to previously acquired 1-^-1m airborne images, ground-level multispectral FLIR images, and the field spectra. Mauna Loa was chosen in large part because the 4x6km summit caldera, flooded with fresh basalt in 1984, appears to be spectrally homogeneous at scales with 10^-1 and 10²m, facilitating the comparison of sensed temperature. The validation results suggest that, with careful atmospheric compensation, it is possible to match ground measurements with measurements from space, and to use the Mauna Loa validation site for cross-comparison of thermal infrared sensors and temperature/emissivity extraction algorithms.

PROCEEDINGS ARTICLE | August 20, 2001

MTI science, data products, and ground-data processing overview

Proc. SPIE. 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII

KEYWORDS: Thermography, Data modeling, Calibration, Satellites, Clouds, Vegetation, Multispectral imaging, Data processing, Algorithm development, Atmospheric modeling

PROCEEDINGS ARTICLE | August 20, 2001

Recipes for writing algorithms to retrieve columnar water vapor for three-band multispectral data

Proc. SPIE. 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII

KEYWORDS: Thermography, MODIS, Sensors, Aerosols, Reflectivity, Atmospheric modeling, Temperature metrology, Radiative transfer, Absorption

PROCEEDINGS ARTICLE | August 20, 2001

Comparison of four methods for determining precipitable water vapor content from multispectral data

Proc. SPIE. 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII

KEYWORDS: Sun, Aerosols, Remote sensing, Reflectivity, Clouds, Photometry, Chlorine, Space operations, Atmospheric sensing, Absorption

PROCEEDINGS ARTICLE | August 20, 2001

Recipes for writing algorithms for atmospheric corrections and temperature/emissivity separations in the thermal regime for a multispectral sensor

Proc. SPIE. 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII

KEYWORDS: Optical filters, Sensors, Calibration, Water, Skin, Physics, Atmospheric corrections, Atmospheric physics, Temperature metrology

PROCEEDINGS ARTICLE | November 17, 2000

Implementation and validation of atmospheric compensation algorithms for Multispectral Thermal Imager (MTI) pipeline processing

Proc. SPIE. 4125, Propagation and Imaging through the Atmosphere IV

KEYWORDS: Thermography, Sun, Aerosols, Image processing, Reflectivity, Vegetation, Algorithm development, Atmospheric particles, Atmospheric modeling, Absorption

PROCEEDINGS ARTICLE | December 17, 1999

Subpixel temperature retrieval with multispectral sensors

Proc. SPIE. 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications

KEYWORDS: Thermography, Signal to noise ratio, Infrared sensors, Mid-IR, MODIS, Sensors, Calibration, Error analysis, Infrared radiation, Temperature metrology

PROCEEDINGS ARTICLE | October 27, 1999

MTI ground data processing and science retrieval pipeline architecture

Proc. SPIE. 3753, Imaging Spectrometry V

KEYWORDS: Data modeling, Calibration, Data storage, Databases, Image processing, Vegetation, Data acquisition, Data processing, Algorithm development, Atmospheric modeling

PROCEEDINGS ARTICLE | October 27, 1999

Multispectral Thermal Imager mission overview

Proc. SPIE. 3753, Imaging Spectrometry V

KEYWORDS: Thermography, Sensors, Calibration, Satellites, Remote sensing, Multispectral imaging, Spatial resolution, Atmospheric sensing, Atmospheric modeling, Systems modeling

PROCEEDINGS ARTICLE | October 27, 1999

Temperature estimation and compositional mapping using spectral mixture analysis of thermal imaging spectrometry data

Proc. SPIE. 3753, Imaging Spectrometry V

KEYWORDS: Thermography, Sensors, Error analysis, Image analysis, Infrared radiation, Statistical modeling, Atmospheric modeling, Shape memory alloys, Temperature metrology, Virtual colonoscopy

PROCEEDINGS ARTICLE | October 27, 1999

MTI core science retrieval algorithms

Proc. SPIE. 3753, Imaging Spectrometry V

KEYWORDS: Near infrared, Reflectivity, Computer simulations, Clouds, Vegetation, Atmospheric corrections, Algorithm development, Atmospheric sciences, Atmospheric modeling, Absorption

PROCEEDINGS ARTICLE | July 16, 1999

Subpixel temperature retrieval with multispectral sensors

Proc. SPIE. 3717, Algorithms for Multispectral and Hyperspectral Imagery V

KEYWORDS: Long wavelength infrared, Thermography, Signal to noise ratio, Mid-IR, MODIS, Sensors, Calibration, Error analysis, Infrared radiation, Temperature metrology

PROCEEDINGS ARTICLE | July 16, 1999

Practical atmospheric correction algorithms for a multispectral sensor from the visible through the thermal spectral regions

Proc. SPIE. 3717, Algorithms for Multispectral and Hyperspectral Imagery V

KEYWORDS: Visible radiation, Sensors, Aerosols, Reflectivity, Clouds, Vegetation, Ocean optics, Atmospheric corrections, Atmospheric particles, Atmospheric optics

PROCEEDINGS ARTICLE | July 16, 1999

Comparing robust and physics-based sea surface temperature retrievals for high-resolution multispectral thermal sensors using one or multiple looks

Proc. SPIE. 3717, Algorithms for Multispectral and Hyperspectral Imagery V

KEYWORDS: Thermography, Sensors, Aerosols, Clouds, Atmospheric physics, Tantalum, Atmospheric particles, Atmospheric modeling, Temperature metrology

PROCEEDINGS ARTICLE | July 12, 1999

Design considerations, modeling, and analysis for the multispectral thermal imager

Proc. SPIE. 3701, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing X

KEYWORDS: Thermography, Signal to noise ratio, Data modeling, Sensors, Calibration, Satellites, Multispectral imaging, Thermal modeling, Atmospheric modeling, Systems modeling

PROCEEDINGS ARTICLE | October 16, 1998

Synthetic hyperspectral data cubes for complex thermal scenes

Proc. SPIE. 3438, Imaging Spectrometry IV

KEYWORDS: FT-IR spectroscopy, Data modeling, Sensors, 3D modeling, Monte Carlo methods, Ray tracing, Fractal analysis, Scene simulation, Thermal modeling, Atmospheric modeling

PROCEEDINGS ARTICLE | October 16, 1998

Multispectral thermal imaging

Proc. SPIE. 3438, Imaging Spectrometry IV

KEYWORDS: Staring arrays, Thermography, Telescopes, Mirrors, Sensors, Calibration, Satellites, Remote sensing, Infrared radiation, Atmospheric modeling

PROCEEDINGS ARTICLE | August 26, 1998

Multi-spectral band selection for satellite-based systems

Proc. SPIE. 3377, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing IX

KEYWORDS: Long wavelength infrared, Mid-IR, Near infrared, Short wave infrared radiation, Visible radiation, Sensors, Satellites, Vegetation, Atmospheric sensing, Absorption

PROCEEDINGS ARTICLE | June 17, 1996

Water vapor retrieval over many surface types

Proc. SPIE. 2758, Algorithms for Multispectral and Hyperspectral Imagery II

KEYWORDS: Signal to noise ratio, Minerals, Sensors, Aerosols, Reflectivity, Atmospheric sensing, Atmospheric particles, Americium, Radiative transfer, Absorption

PROCEEDINGS ARTICLE | June 17, 1996

Accurate top-of-the-atmosphere albedo determination from multiple views of the MISR instrument

Proc. SPIE. 2758, Algorithms for Multispectral and Hyperspectral Imagery II

KEYWORDS: Near infrared, Sun, Data modeling, Cameras, Reflectivity, Atmospheric corrections, Bidirectional reflectance transmission function, Earth's atmosphere, Atmospheric modeling, Radiative transfer

PROCEEDINGS ARTICLE | June 10, 1996

Methodology for rapid infrared multispectral electro-optical imaging system performance analysis and synthesis

Proc. SPIE. 2743, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VII

KEYWORDS: Signal to noise ratio, Optical transfer functions, Optical filters, Imaging systems, Sensors, Interference (communication), Electro optical systems, Electro optical imaging, Signal detection, Atmospheric optics

PROCEEDINGS ARTICLE | June 10, 1996

End-to-end performance modeling of passive remote sensing systems

Proc. SPIE. 2743, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing VII

KEYWORDS: Data modeling, Sensors, Multispectral imaging, Sensing systems, Algorithm development, Atmospheric sensing, Passive remote sensing, Atmospheric modeling, Systems modeling, Device simulation

PROCEEDINGS ARTICLE | May 31, 1996

Partial removal of correlated noise in thermal imagery

Proc. SPIE. 2759, Signal and Data Processing of Small Targets 1996

KEYWORDS: Thermography, Signal to noise ratio, Optical transfer functions, Imaging systems, Sensors, Scanners, Fourier transforms, Interference (communication), Image filtering, Algorithm development

PROCEEDINGS ARTICLE | May 31, 1996

Image-quality degradation and retrieval errors introduced by registration and interpolation of multispectral digital images

Proc. SPIE. 2759, Signal and Data Processing of Small Targets 1996

KEYWORDS: Sensors, Scanners, Computer simulations, Image registration, Multispectral imaging, Image quality, Image retrieval, Convolution, Modulation transfer functions, Space operations

PROCEEDINGS ARTICLE | August 1, 1992

Adjacency-blurring effect of scenes modeled by the radiosity method

Proc. SPIE. 1688, Atmospheric Propagation and Remote Sensing

KEYWORDS: Point spread functions, Scattering, Sensors, Light scattering, Reflectivity, Monte Carlo methods, Bidirectional reflectance transmission function, Scene simulation, Atmospheric particles, Atmospheric modeling

PROCEEDINGS ARTICLE | July 1, 1991

Simulation of partially obscured scenes using the radiosity method

Proc. SPIE. 1486, Characterization, Propagation, and Simulation of Sources and Backgrounds

KEYWORDS: Infrared imaging, Fiber optic gyroscopes, Scattering, Sensors, Reflectivity, Clouds, Multiple scattering, Monte Carlo methods, Chemical elements, Radio propagation

Showing 5 of 50 publications

Conference Committee Involvement (20)

Remote Sensing of Clouds and the Atmosphere

12 September 2018 | Berlin, Germany

Remote Sensing of Clouds and the Atmosphere

13 September 2017 | Warsaw, Poland

Remote Sensing of Clouds and the Atmosphere

28 September 2016 | Edinburgh, United Kingdom

Imaging Spectrometry XXI

29 August 2016 | San Diego, California, United States

Remote Sensing of Clouds and the Atmosphere

23 September 2015 | Toulouse, France

Showing 5 of 20 published special sections

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