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Dive into the research topics where Michael L. Hoke is active.

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Featured researches published by Michael L. Hoke.


Algorithms for multispectral, hyperspectral, and ultraspectral imagery. Conference | 2000

Status of atmospheric correction using a MODTRAN4-based algorithm

Michael W. Matthew; Steven M. Adler-Golden; Alexander Berk; Steven C. Richtsmeier; Robert Y. Levine; Lawrence S. Bernstein; Prabhat K. Acharya; Gail P. Anderson; Gerald W. Felde; Michael L. Hoke; Anthony J. Ratkowski; Hsiao-hua K. Burke; Robert D. Kaiser; David P. Miller

The present disclosure is directed to maintaining horizontal alignment of peeling and cleaning rolls in a shrimp peeler and cleaner by restricting rotary movement of the posts without inhibiting up and down movement of adjacent posts at the base of which are journalled the peeling rolls. This is accomplished by molding flat walled projections at the base of the posts over which are received a locking member having a plurality of openings therethrough the walls of which are complemental to the flat walls of the projections in one direction and which are slightly greater in the other direction to permit relative vertical movement between adjacent posts.


Remote Sensing | 2004

MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options

Alexander Berk; Gail P. Anderson; Prabhat K. Acharya; Lawrence S. Bernstein; Leon Muratov; Jamine Lee; Marsha J. Fox; Steve M. Adler-Golden; James H. Chetwynd; Michael L. Hoke; Ronald B. Lockwood; James A. Gardner; Thomas W. Cooley; Christoph C. Borel; Paul E. Lewis

The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances.


Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X | 2004

The sequential maximum angle convex cone (SMACC) endmember model

John H. Gruninger; Anthony J. Ratkowski; Michael L. Hoke

A new endmember extraction method has been developed that is based on a convex cone model for representing vector data. The endmembers are selected directly from the data set. The algorithm for finding the endmembers is sequential: the convex cone model starts with a single endmember and increases incrementally in dimension. Abundance maps are simultaneously generated and updated at each step. A new endmember is identified based on the angle it makes with the existing cone. The data vector making the maximum angle with the existing cone is chosen as the next endmember to add to enlarge the endmember set. The algorithm updates the abundances of previous endmembers and ensures that the abundances of previous and current endmembers remain positive or zero. The algorithm terminates when all of the data vectors are within the convex cone, to some tolerance. The method offers advantages for hyperspectral data sets where high correlation among channels and pixels can impair un-mixing by standard techniques. The method can also be applied as a band-selection tool, finding end-images that are unique and forming a convex cone for modeling the remaining hyperspectral channels. The method is described and applied to hyperspectral data sets.


Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII | 2006

MODTRAN5: 2006 update

Alexander Berk; Gail P. Anderson; Prabhat K. Acharya; Lawrence S. Bernstein; Leon Muratov; Jamine Lee; Marsha J. Fox; Steve M. Adler-Golden; James H. Chetwynd; Michael L. Hoke; Ronald B. Lockwood; James A. Gardner; Thomas W. Cooley; Christoph C. Borel; Paul E. Lewis; Eric P. Shettle

The MODTRAN5 radiation transport (RT) model is a major advancement over earlier versions of the MODTRAN atmospheric transmittance and radiance model. New model features include (1) finer spectral resolution via the Spectrally Enhanced Resolution MODTRAN (SERTRAN) molecular band model, (2) a fully coupled treatment of auxiliary molecular species, and (3) a rapid, high fidelity multiple scattering (MS) option. The finer spectral resolution improves model accuracy especially in the mid- and long-wave infrared atmospheric windows; the auxiliary species option permits the addition of any or all of the suite of HITRAN molecular line species, along with default and user-defined profile specification; and the MS option makes feasible the calculation of Vis-NIR databases that include high-fidelity scattered radiances. Validations of the new band model algorithms against line-by-line (LBL) codes have proven successful.


Remote Sensing | 1999

MODTRAN4: radiative transfer modeling for remote sensing

Gail P. Anderson; Alexander Berk; Prabhat K. Acharya; Michael W. Matthew; Lawrence S. Bernstein; James H. Chetwynd; H. Dothe; Steven M. Adler-Golden; Anthony J. Ratkowski; Gerald W. Felde; James A. Gardner; Michael L. Hoke; Steven C. Richtsmeier; Brian Pukall; Jason B. Mello; Laila S. Jeong

MODTRAN4, the newly released version of the U.S. Air Force atmospheric transmission, radiance and flux model is being developed jointly by the Air Force Research Laboratory/Space Vehicles Directorate and Spectral Sciences, Inc. It is expected to provide the accuracy required for analyzing spectral data for both atmospheric and surface characterization. These two quantities are the subject of satellite and aircraft campaigns currently being developed and pursued by, for instance: NASA (Earth Observing System), NPOESS (National Polar Orbiting Environmental Satellite System), and the European Space Agency (GOME--Global Ozone Monitoring Experiment). Accuracy improvements in MODTRAN relate primarily to two major developments: (1) the multiple scattering algorithms have been made compatible with the spectroscopy by adopting a corrected-k approach to describe the statistically expected transmittance properties for each spectral bin and atmospheric layer, and (2) radiative transfer calculations can be conducted with a Beer-Lambert formulation that improves the treatment of path inhomogeneities. Other code enhancements include the incorporation of solar azimuth dependence in the DISORT- based multiple scattering model, the introduction of surface BRDF (Bi-directional Radiance Distribution Functions) models and 15 cm-1 band model for improved computational speed.


Optical spectroscopic techniques and instrumentation for atmospheric and space research. Conference | 1999

MODTRAN4 radiative transfer modeling for atmospheric correction

Alexander Berk; Gail P. Anderson; Lawrence S. Bernstein; Prabhat K. Acharya; H. Dothe; Michael W. Matthew; Steven M. Adler-Golden; James H. Chetwynd; Steven C. Richtsmeier; Brian Pukall; Clark L. Allred; Laila S. Jeong; Michael L. Hoke

MODTRAN4, the latest publicly released version of MODTRAN, provides many new and important options for modeling atmospheric radiation transport. A correlated-k algorithm improves multiple scattering, eliminates Curtis-Godson averaging, and introduces Beers Law dependencies into the band model. An optimized 15 cm-1 band model provides over a 10-fold increase in speed over the standard MODTRAN 1 cm-1 band model with comparable accuracy when higher spectral resolution results are unnecessary. The MODTRAN ground surface has been upgraded to include the effects of Bidirectional Reflectance Distribution Functions (BRDFs) and Adjacency. The BRDFs are entered using standard parameterizations and are coupled into line-of-sight surface radiance calculations.


Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery. Conference | 2005

Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery

Lawrence S. Bernstein; Steven M. Adler-Golden; Robert Sundberg; Robert Y. Levine; Timothy Perkins; Alexander Berk; Anthony J. Ratkowski; Gerald W. Felde; Michael L. Hoke

We describe a new visible-near infrared short-wavelength infrared (VNIR-SWIR) atmospheric correction method for multi- and hyperspectral imagery, dubbed QUAC (QUick Atmospheric Correction) that also enables retrieval of the wavelength-dependent optical depth of the aerosol or haze and molecular absorbers. It determines the atmospheric compensation parameters directly from the information contained within the scene using the observed pixel spectra. The approach is based on the empirical finding that the spectral standard deviation of a collection of diverse material spectra, such as the endmember spectra in a scene, is essentially spectrally flat. It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does not have a proper radiometric or wavelength calibration, or when the solar illumination intensity is unknown. The computational speed of the atmospheric correction method is significantly faster than for the first-principles methods, making it potentially suitable for real-time applications. The aerosol optical depth retrieval method, unlike most prior methods, does not require the presence of dark pixels. QUAC is applied to atmospherically correction several AVIRIS data sets and a Landsat-7 data set, as well as to simulated HyMap data for a wide variety of atmospheric conditions. Comparisons to the physics-based Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) code are also presented.


Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII | 2002

MODTRAN4-based atmospheric correction algorithm: FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes)

Gail P. Anderson; Gerald W. Felde; Michael L. Hoke; Anthony J. Ratkowski; Thomas W. Cooley; James H. Chetwynd; James A. Gardner; Steven M. Adler-Golden; Michael W. Matthew; Alexander Berk; Lawrence S. Bernstein; Prabhat K. Acharya; David P. Miller; Paul E. Lewis

Terrain categorization and target detection algorithms applied to Hyperspectral Imagery (HSI) typically operate on the measured reflectance (of sun and sky illumination) by an object or scene. Since the reflectance is a non-dimensional ratio, the reflectance by an object is nominally not affedted by variations in lighting conditions. Atmospheric Correction (also referred to as Atmospheric Compensation, Characterization, etc.) Algorithms (ACAs) are used in application of remotely sensed HSI datat to correct for the effects of atmospheric propagation on measurements acquired by air and space-borne systems. The Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm is an ACA created for HSI applications in the visible through shortwave infrared (Vis-SWIR) spectral regime. FLAASH derives its physics-based mathematics from MODTRAN4.


Algorithms for multispectral, hyperspectral, and ultraspectral imagery. Conference | 2000

MODTRAN4 : Radiative transfer modeling for remote sensing

Gail P. Anderson; Alexander Berk; Prabhat K. Acharya; Michael W. Matthew; Lawrence S. Bernstein; James H. Chetwynd; H. Dothe; Steven M. Adler-Golden; Anthony J. Ratkowski; Gerald W. Felde; James A. Gardner; Michael L. Hoke; Steven C. Richtsmeier; Brian Pukall; Jason B. Mello; Laila S. Jeong

MODTRAN4, the newly released version of the U.S. Air Force atmospheric transmission, radiance and flux model is being developed jointly by the Air Force Research Laboratory / Space Vehicles Directorate (AFRL / VS) and Spectral Sciences, Inc. It is expected to provide the accuracy required for analyzing spectral data for both atmospheric and surface characterization. These two quantities are the subject of satellite and aircraft campaigns currently being developed and pursued by, for instance: NASA (Earth Observing System), NPOESS (National Polar Orbiting Environmental Satellite System), and the European Space Agency (GOME - Global Ozone Monitoring Experiment). Accuracy improvements in MODTRAN relate primarily to two major developments: (1) the multiple scattering algorithms have been made compatible with the spectroscopy by adopting a correlated-^ approach to describe the statistically expected transmittance properties for each spectral bin and atmospheric layer, and (2) radiative transfer calculations can be conducted with a Beer-Lambert formulation that improves the treatment of path inhomogeneities. Other code enhancements include the incorporation of solar azimuth dependence in the DISORT-based multiple scattering model, the introduction of surface BRDF (Bi-directional Radiance Distribution Functions) models and a 15 cm-1 band model for improved computational speed. Finally, recent changes to the HITRAN data base, relevant to the 0.94 and 1.13 um bands of water vapor, have been incorporated into the MODTRAN4 databases.


Proceedings of SPIE | 2001

Shadow-insensitive material detection/classification with atmospherically corrected hyperspectral imagery

Steven M. Adler-Golden; Robert Y. Levine; Michael W. Matthew; Steven C. Richtsmeier; Lawrence S. Bernstein; John H. Gruninger; Gerald W. Felde; Michael L. Hoke; Gail P. Anderson; Anthony J. Ratkowski

Shadow-insensitive detection or classification of surface materials in atmospherically corrected hyperspectral imagery can be achieved by expressing the reflectance spectrum as a linear combination of spectra that correspond to illumination by the direct sum and by the sky. Some specific algorithms and applications are illustrated using HYperspectral Digital Imagery Collection Experiment (HYDICE) data.

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Alexander Berk

Spectral Sciences Incorporated

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Gail P. Anderson

Air Force Research Laboratory

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Lawrence S. Bernstein

Spectral Sciences Incorporated

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Anthony J. Ratkowski

Air Force Research Laboratory

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Prabhat K. Acharya

Spectral Sciences Incorporated

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Steven M. Adler-Golden

Spectral Sciences Incorporated

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James A. Gardner

Air Force Research Laboratory

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James H. Chetwynd

Air Force Research Laboratory

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Michael W. Matthew

Spectral Sciences Incorporated

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Gerald W. Felde

Air Force Research Laboratory

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