Dr. Dimitris G. Manolakis Profile

Dr. Dimitris G. Manolakis

Senior Technical Staff Member at MIT Lincoln Lab

SPIE Involvement:

Author

Publications (61)

Proceedings Article | 16 June 2023 Presentation + Paper

Multivariate air quality time series analysis via a recurrent variational deep learning model

Cooper Loughlin, Dimitris Manolakis, Vinay Ingle

Proceedings Volume 12525, 125250G (2023) https://doi.org/10.1117/12.2663201

KEYWORDS: Data modeling, Air quality, Histograms, Statistical modeling, Autoregressive models, Neural networks, Time series analysis, Mixtures, Deep learning

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Proceedings Article | 14 June 2023 Presentation

Concurrent atmospheric retrieval and wavelength calibration correction technique for improved emissivity retrieval

M. Pieper, O. McElhinney, D. Manolakis, R. Bostick, A. Weisner

Proceedings Volume PC12519, PC1251908 (2023) https://doi.org/10.1117/12.2663931

KEYWORDS: Atmospheric modeling, Calibration, Atmospheric corrections, Solid modeling, Atmospheric propagation, Model-based design, Long wavelength infrared, Data modeling, Solids, Sensors

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Proceedings Article | 13 June 2023 Presentation + Paper

Spectral variability modeling with variational autoencoders for hyperspectral target analysis

Cooper Loughlin, Dimitris Manolakis, Michael Pieper, Vinay Ingle, Randall Bostick, Andrew Weisner

Proceedings Volume 12519, 125190N (2023) https://doi.org/10.1117/12.2663195

KEYWORDS: Target detection, Data modeling, Machine learning, Mixtures, Hyperspectral target detection, Detection and tracking algorithms, Statistical analysis

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Proceedings Article | 31 May 2022 Presentation + Paper

Improved sub-pixel material identification with spline-based spectral smoothing

O. McElhinney, M. Pieper, D. Manolakis, V. Ingle, C. Loughlin, Randall Bostick, A. Weisner

Proceedings Volume 12094, 120940G (2022) https://doi.org/10.1117/12.2618540

KEYWORDS: Data modeling, Sensors, Detection and tracking algorithms, Smoothing, Cameras, Signal to noise ratio, Hyperspectral imaging, Statistical analysis, Error analysis, Algorithm development

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Proceedings Article | 31 May 2022 Presentation + Paper

Spline based emissivity retrieval for LWIR hyperspectral imagery

O. McElhinney, M. Pieper, D. Manolakis, C. Loughlin, V. Ingle, Randall Bostick, A. Weisner

Proceedings Volume 12094, 120940N (2022) https://doi.org/10.1117/12.2618553

KEYWORDS: Error analysis, Sensors, Long wavelength infrared, Hyperspectral imaging, Temperature metrology, Solids, Smoothing, Black bodies, Spectral resolution, Feature extraction

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Proceedings Article | 6 September 2019 Presentation + Paper

On clairvoyant and universal tests for hyperspectral target detection

D. Manolakis, E. Truslow, M. Pieper, A. Weisner, R. Bostick, T. Cooley

Proceedings Volume 11130, 1113004 (2019) https://doi.org/10.1117/12.2529979

KEYWORDS: Sensors, Composites, Hyperspectral target detection, Detection and tracking algorithms, Binary data, Algorithm development, Lawrencium, Analytical research, Target detection, Detector development

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Proceedings Article | 18 September 2018 Presentation + Paper

Wavelength calibration correction for ground radiance spectra in LWIR hyperspectral imagery

M. Pieper, D. Manolakis, E. Truslow, J. Jacobson, A. Weisner, T. Cooley, V. Ingle

Proceedings Volume 10768, 1076805 (2018) https://doi.org/10.1117/12.2321332

KEYWORDS: Calibration, Error analysis, Reflectivity, Atmospheric modeling, Sensors, Long wavelength infrared, Hyperspectral imaging, Atmospheric sensing, Black bodies, Solids

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Proceedings Article | 8 May 2018 Presentation + Paper

Sensitivity of temperature and emissivity separation to atmospheric errors in LWIR hyperspectral imagery

M. Pieper, D. Manolakis, E. Truslow, J. Jacobson, A. Weisner, T. Cooley, V. Ingle

Proceedings Volume 10644, 106440R (2018) https://doi.org/10.1117/12.2303877

KEYWORDS: Atmospheric modeling, Error analysis, Sensors, Atmospheric sensing, Long wavelength infrared, Performance modeling, Calibration, Hyperspectral imaging

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SPIE Journal Paper | 29 May 2017

Performance limitations of temperature–emissivity separation techniques in long-wave infrared hyperspectral imaging applications

Michael Pieper, Dimitris Manolakis, Eric Truslow, Thomas Cooley, Michael Brueggeman, John Jacobson, Andrew Weisner

OE, Vol. 56, Issue 08, 081804, (May 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.8.081804

KEYWORDS: Infrared imaging, Infrared radiation, Hyperspectral imaging, Long wavelength infrared, Thermography, Sensors, Algorithm development, Solid modeling, Performance modeling, Data acquisition

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Proceedings Article | 19 September 2016 Presentation + Paper

In-scene LWIR downwelling radiance estimation

M. Pieper, D. Manolakis, E. Truslow, T. Cooley, M. Brueggeman, A. Weisner, J. Jacobson

Proceedings Volume 9976, 99760E (2016) https://doi.org/10.1117/12.2239138

KEYWORDS: Atmospheric sensing, Sensors, Long wavelength infrared, Atmospheric modeling, Calibration, Error analysis, Sensor calibration, Black bodies, Gases, Absorption

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Proceedings Article | 19 September 2016 Presentation + Paper

Statistical modeling of natural backgrounds in hyperspectral LWIR data

Eric Truslow, Dimitris Manolakis, Thomas Cooley, Joseph Meola

Proceedings Volume 9976, 99760H (2016) https://doi.org/10.1117/12.2239432

KEYWORDS: Data modeling, Long wavelength infrared, Statistical analysis, Statistical modeling, Infrared sensors, Sensors, Reflectivity, Atmospheric modeling, Black bodies, Target detection

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Proceedings Article | 17 May 2016 Paper

Chemical plume detection with an iterative background estimation technique

Eric Truslow, Steven Golowich, Dimitris Manolakis

Proceedings Volume 9840, 98401M (2016) https://doi.org/10.1117/12.2224350

KEYWORDS: Long wavelength infrared, Sensors, Contamination, Chemical analysis, Statistical analysis, Binary data, Image processing, Image filtering, Detection and tracking algorithms, Gaussian filters

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SPIE Journal Paper | 10 February 2016

Performance metrics for the evaluation of hyperspectral chemical identification systems

Eric Truslow, Steven Golowich, Dimitris Manolakis, Vinay Ingle

OE, Vol. 55, Issue 02, 023106, (February 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.2.023106

KEYWORDS: Long wavelength infrared, Sensors, Curium, Detection and tracking algorithms, Data modeling, Gases, Optical engineering, Chemical analysis, Performance modeling, System identification

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Proceedings Article | 1 September 2015 Paper

Comparison of hyperspectral change detection algorithms

M. Pieper, D. Manolakis, E. Truslow, T. Cooley, M. Brueggeman, A. Weisner, J. Jacobson

Proceedings Volume 9611, 96110Z (2015) https://doi.org/10.1117/12.2188316

KEYWORDS: Transform theory, Simulation of CCA and DLA aggregates, Principal component analysis, Sensors, Detection and tracking algorithms, Neodymium, Matrices, Hyperspectral imaging, Advanced distributed simulations, Defense technologies

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Proceedings Article | 21 May 2015 Paper

Metrics for the comparative evaluation of chemical plume identification algorithms

E. Truslow, S. Golowich, D. Manolakis, V. Ingle

Proceedings Volume 9472, 947203 (2015) https://doi.org/10.1117/12.2177163

KEYWORDS: Sensors, Gases, Detection and tracking algorithms, Data modeling, Infrared sensors, Long wavelength infrared, Algorithm development, Curium, Transmittance, Performance modeling

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Proceedings Article | 13 June 2014 Paper

Hyperspectral chemical plume quantification and temperature estimation

Sidi Niu, Steven Golowich, Vinay Ingle, Dimitris Manolakis

Proceedings Volume 9088, 90880V (2014) https://doi.org/10.1117/12.2049980

KEYWORDS: Chemical analysis, Error analysis, Temperature metrology, Detection and tracking algorithms, Modulation, Absorption, Gases, Solids, Transmittance, Hyperspectral imaging

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SPIE Journal Paper | 23 December 2013

New approach to remote gas-phase chemical quantification: selected-band algorithm

Sidi Niu, Steven Golowich, Vinay Ingle, Dimitris Manolakis

OE, Vol. 53, Issue 02, 021111, (December 2013) https://doi.org/10.1117/12.10.1117/1.OE.53.2.021111

KEYWORDS: Error analysis, Chemical analysis, Absorption, Principal component analysis, Gases, Transmittance, Optical engineering, Sensors, Algorithm development, Computer simulations

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SPIE Journal Paper | 16 December 2013

Cramer-Rao bounds for long-wave infrared gaseous plume quantification

Steven Golowich, Dimitris Manolakis

OE, Vol. 53, Issue 02, 021109, (December 2013) https://doi.org/10.1117/12.10.1117/1.OE.53.2.021109

KEYWORDS: Sensors, Long wavelength infrared, Black bodies, Infrared radiation, Chromium, Optical engineering, Solids, Temperature metrology, Transmittance, Chemical analysis

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Proceedings Article | 22 October 2013 Paper

Efficient implementations of hyperspectral chemical-detection algorithms

Cory J. Brett, Robert DiPietro, Dimitris Manolakis, Vinay Ingle

Proceedings Volume 8897, 88970T (2013) https://doi.org/10.1117/12.2028562

KEYWORDS: Sensors, MATLAB, Mahalanobis distance, Clouds, Detection and tracking algorithms, C++, Hyperspectral imaging, Long wavelength infrared, Linear algebra, Spectral data processing

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Proceedings Article | 18 May 2013 Paper

The remarkable success of adaptive cosine estimator in hyperspectral target detection

D. Manolakis, M. Pieper, E. Truslow, T. Cooley, M. Brueggeman, S. Lipson

Proceedings Volume 8743, 874302 (2013) https://doi.org/10.1117/12.2015392

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Hyperspectral target detection, Data modeling, Algorithm development, Statistical analysis, Performance modeling, Reflectivity, Hyperspectral imaging

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Proceedings Article | 18 May 2013 Paper

False alarm mitigation techniques for hyperspectral target detection

M. Pieper, D. Manolakis, E. Truslow, T. Cooley, M. Brueggeman

Proceedings Volume 8743, 874304 (2013) https://doi.org/10.1117/12.2015906

KEYWORDS: Sensors, Monte Carlo methods, Hyperspectral imaging, Detection and tracking algorithms, Stereolithography, Hyperspectral target detection, Analytical research, Spatial resolution, Niobium, Statistical analysis

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Proceedings Article | 18 May 2013 Paper

Hyperspectral chemical plume quantification via background radiance estimation

Sidi Niu, Steven Golowich, Vinay Ingle, Dimitris Manolakis

Proceedings Volume 8743, 874316 (2013) https://doi.org/10.1117/12.2016197

KEYWORDS: Error analysis, Absorption, Transmittance, Chemical analysis, Tantalum, Principal component analysis, Atmospheric sensing, Sensors, Modulation, Hyperspectral imaging

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SPIE Journal Paper | 4 February 2013

Implications and mitigation of model mismatch and covariance contamination for hyperspectral chemical agent detection

Sidi Niu, Steven Golowich, Vinay Ingle, Dimitris Manolakis

OE, Vol. 52, Issue 2, 026202, (February 2013) https://doi.org/10.1117/12.10.1117/1.OE.52.2.026202

KEYWORDS: Contamination, Sensors, Optical engineering, Signal attenuation, Detection and tracking algorithms, Transmittance, Mahalanobis distance, Long wavelength infrared, Hyperspectral imaging, Absorption

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Proceedings Article | 25 October 2012 Paper

False-alarm characterization in hyperspectral gas-detection applications

Robert DiPietro, Eric Truslow, Dimitris Manolakis, Steven Golowich, Ronald Lockwood

Proceedings Volume 8515, 85150I (2012) https://doi.org/10.1117/12.929037

KEYWORDS: Sensors, Data modeling, Mahalanobis distance, Long wavelength infrared, Clouds, Systems modeling, Statistical analysis, Infrared sensors, Electroluminescent displays, Detection and tracking algorithms

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Proceedings Article | 24 May 2012 Paper

Algorithms for remote quantification of chemical plumes: a comparative study

Sidi Niu, Steven Golowich, Dimitris Manolakis

Proceedings Volume 8390, 83902I (2012) https://doi.org/10.1117/12.919557

KEYWORDS: Atmospheric modeling, Sensors, Transmittance, Electrophoretic light scattering, Terbium, Neptunium, Long wavelength infrared, Hyperspectral imaging, Data modeling, Tantalum

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SPIE Journal Paper | 10 February 2012

Hyperspectral matched filter with false-alarm mitigation

Robert DiPietro, Dimitris Manolakis, Ronald Lockwood, Thomas Cooley, John Jacobson

OE, Vol. 51, Issue 1, 016202, (February 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.1.016202

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Mahalanobis distance, Optical filters, Hyperspectral target detection, Optical engineering, Hyperspectral imaging, Algorithm development, Electronic filtering

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Proceedings Article | 6 October 2011 Paper

Design and evaluation of robust matched filters for chemical agent detection

Sidi Niu, Steven Golowich, Vinay Ingle, Dimitris Manolakis

Proceedings Volume 8186, 81860R (2011) https://doi.org/10.1117/12.898094

KEYWORDS: Sensors, Contamination, Absorption, Atmospheric modeling, Hyperspectral imaging, Transmittance, Detection and tracking algorithms, Modulation, Algorithm development, Electro optical modeling

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Proceedings Article | 7 September 2011 Paper

Hyperspectral detection and discrimination using the ACE algorithm

M. Pieper, D. Manolakis, R. Lockwood, T. Cooley, P. Armstrong, J. Jacobson

Proceedings Volume 8158, 815807 (2011) https://doi.org/10.1117/12.893950

KEYWORDS: Sensors, Target detection, Hyperspectral imaging, Mahalanobis distance, Detection and tracking algorithms, Analytical research, Roads, Algorithm development, Electromagnetism, Imaging systems

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Proceedings Article | 20 May 2011 Paper

Performance limits of LWIR gaseous plume quantification

Steven Golowich, Dimitris Manolakis

Proceedings Volume 8048, 80481F (2011) https://doi.org/10.1117/12.884360

KEYWORDS: Sensors, Black bodies, Long wavelength infrared, Terbium, Chromium, Transmittance, Chemical analysis, Atmospheric modeling, Tantalum, Temperature metrology

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Proceedings Article | 20 May 2011 Paper

Implications of model mismatch and covariance contamination on chemical detection algorithms

Sidi Niu, Steven Golowich, Vinay Ingle, Dimitris Manolakis

Proceedings Volume 8048, 80481E (2011) https://doi.org/10.1117/12.883400

KEYWORDS: Contamination, Sensors, Detection and tracking algorithms, Signal attenuation, Transmittance, Filtering (signal processing), Absorption, Atmospheric modeling, Tantalum, Mahalanobis distance

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Proceedings Article | 13 August 2010 Paper

Tests for the elliptical symmetry of hyperspectral imaging data

Sidi Niu, Vinay Ingle, Dimitris Manolakis, Thomas Cooley

Proceedings Volume 7812, 78120D (2010) https://doi.org/10.1117/12.861055

KEYWORDS: Data modeling, Spherical lenses, Hyperspectral imaging, Statistical analysis, Algorithm development, Statistical modeling, Mahalanobis distance, Geographic information systems, Performance modeling, Analytical research

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Proceedings Article | 13 May 2010 Paper

Performance evaluation of hyperspectral detection algorithms for subpixel objects

R. DiPietro, D. Manolakis, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 7695, 76951W (2010) https://doi.org/10.1117/12.850036

KEYWORDS: Sensors, Detection and tracking algorithms, Target detection, Hyperspectral imaging, Mahalanobis distance, Statistical analysis, Signal detection, Electronic filtering, Hyperspectral target detection, Performance modeling

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Proceedings Article | 18 August 2009 Paper

Hyperspectral detection algorithms: use covariances or subspaces?

D. Manolakis, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 7457, 74570Q (2009) https://doi.org/10.1117/12.828397

KEYWORDS: Detection and tracking algorithms, Sensors, Optical filters, Hyperspectral imaging, Statistical analysis, Data modeling, Principal component analysis, Spectral models, Linear filtering, Phased arrays

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Proceedings Article | 27 April 2009 Paper

Is there a best hyperspectral detection algorithm?

D. Manolakis, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 7334, 733402 (2009) https://doi.org/10.1117/12.816917

KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Data modeling, Hyperspectral target detection, Algorithm development, Statistical analysis, Reflectivity, Hyperspectral imaging, Mahalanobis distance

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Proceedings Article | 11 April 2008 Paper

Software algorithms for false alarm reduction in LWIR hyperspectral chemical agent detection

D. Manolakis, J. Model, M. Rossacci, D. Zhang, E. Ontiveros, M. Pieper, J. Seeley, D. Weitz

Proceedings Volume 6966, 69661U (2008) https://doi.org/10.1117/12.775826

KEYWORDS: Sensors, Signal processing, Long wavelength infrared, Statistical analysis, Detection and tracking algorithms, Binary data, Algorithm development, Target detection, Distance measurement

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Proceedings Article | 10 May 2007 Paper

Modeling the effects of atmospheric propagation for spectral libraries of natural backgrounds

Mary Ann Glennon, Gail Anderson, Dimitris Manolakis, Ronald Lockwood, Peggy Grigsby, John Jacobson, John Cipar, Thomas Cooley

Proceedings Volume 6565, 656521 (2007) https://doi.org/10.1117/12.718761

KEYWORDS: Atmospheric modeling, Reflectivity, Data modeling, Statistical modeling, Sensors, Atmospheric sensing, Performance modeling, Atmospheric particles, Atmospheric propagation, Hyperspectral imaging

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Proceedings Article | 10 May 2007 Paper

Maintaining CFAR operation in hyperspectral target detection using extreme value distributions

D. Manolakis, D. Zhang, M. Rossacci, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 6565, 65651W (2007) https://doi.org/10.1117/12.718373

KEYWORDS: Sensors, Statistical analysis, Hyperspectral imaging, Detection and tracking algorithms, Target detection, Hyperspectral target detection, RGB color model, Data modeling, Mahalanobis distance, Space operations

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Proceedings Article | 7 May 2007 Paper

Statistical models for LWIR hyperspectral backgrounds and their applications in chemical agent detection

D. Manolakis, L. Jairam, D. Zhang, M. Rossacci

Proceedings Volume 6565, 656525 (2007) https://doi.org/10.1117/12.718378

KEYWORDS: Sensors, Statistical analysis, Long wavelength infrared, Data modeling, Detection and tracking algorithms, Atmospheric sensing, Clouds, Hyperspectral imaging, Algorithm development

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Proceedings Article | 1 September 2006 Paper

Effects of dimensionality reduction on the statististical distribution of hyperspectral backgrounds

M. Rossacci, D. Manolakis, J. Cipar, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 6302, 63020H (2006) https://doi.org/10.1117/12.680388

KEYWORDS: Mahalanobis distance, Data modeling, Principal component analysis, Sensors, Statistical analysis, Agriculture, Reflectivity, Statistical modeling, Data processing, Target detection

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Proceedings Article | 1 September 2006 Paper

Signal processing algorithms for staring single pixel hyperspectral sensors

Dimitris Manolakis, Michael Rossacci, Erin O'Donnell, Francis D'Amico

Proceedings Volume 6302, 630210 (2006) https://doi.org/10.1117/12.680399

KEYWORDS: Sensors, Clouds, Detection and tracking algorithms, Long wavelength infrared, Signal processing, Algorithm development, Infrared spectroscopy, Signal detection, Signal to noise ratio

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Proceedings Article | 5 November 2005 Paper

Design and evaluation of hyperspectral algorithms for chemical warfare agent detection

Dimitris Manolakis, Francis D'Amico

Proceedings Volume 5995, 599503 (2005) https://doi.org/10.1117/12.629362

KEYWORDS: Sensors, Detection and tracking algorithms, Clouds, Algorithm development, Signal to noise ratio, Statistical analysis, Signal detection, Hyperspectral imaging, Long wavelength infrared

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SPIE Journal Paper | 1 November 2005

Realistic matched filter performance prediction for hyperspectral target detection

Dimitris Manolakis

OE, Vol. 44, Issue 11, 116401, (November 2005) https://doi.org/10.1117/12.10.1117/1.2125487

KEYWORDS: Target detection, Statistical analysis, Sensors, Performance modeling, Hyperspectral target detection, Data modeling, Signal to noise ratio, Optical filters, Hyperspectral imaging, Optical engineering

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SPIE Journal Paper | 1 June 2005

Taxonomy of detection algorithms for hyperspectral imaging applications

Dimitris Manolakis

OE, Vol. 44, Issue 06, 066403, (June 2005) https://doi.org/10.1117/12.10.1117/1.1930927

KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Taxonomy, Hyperspectral imaging, Data modeling, Algorithm development, Optical engineering, Statistical analysis, Mahalanobis distance

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Proceedings Article | 1 June 2005 Paper

Statistical characterization of natural hyperspectral backgrounds using t-elliptically contoured distributions

D. Manolakis, M. Rossacci, J. Cipar, R. Lockwood, T. Cooley, J. Jacobson

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.602315

KEYWORDS: Data modeling, Statistical analysis, Mahalanobis distance, Hyperspectral imaging, Statistical modeling, Agriculture, Short wave infrared radiation, Databases, Vegetation, Detection and tracking algorithms

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Proceedings Article | 12 May 2005 Paper

A taxonomy of algorithms for chemical vapor detection with hyperspectral imaging spectroscopy

Dimitris Manolakis, Francis D'Amico

Proceedings Volume 5795, (2005) https://doi.org/10.1117/12.602323

KEYWORDS: Sensors, Detection and tracking algorithms, Hyperspectral imaging, Data modeling, Atmospheric sensing, Algorithm development, Taxonomy, Spectroscopy, Signal detection, Target detection

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Proceedings Article | 15 October 2004 Paper

Statistical quality assessment criteria for a linear mixing model with elliptical t-distribution errors

Dimitris Manolakis

Proceedings Volume 5546, (2004) https://doi.org/10.1117/12.559496

KEYWORDS: Error analysis, Statistical modeling, Statistical analysis, Data modeling, Atmospheric modeling, Hyperspectral imaging, Gases, Detection and tracking algorithms, Reflectivity, Target detection

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Proceedings Article | 12 August 2004 Paper

Dimensionality reduction of hyperspectral imaging data using local principal components transforms

Dimitris Manolakis, David Marden

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.542081

KEYWORDS: Hyperspectral imaging, Sensors, Reconstruction algorithms, Transform theory, Principal component analysis, Data compression, Data modeling, Data storage, Detection and tracking algorithms, Quantization

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Proceedings Article | 12 August 2004 Paper

Matched filter performance for unequal target and background covariance matrices

Christopher Rozell, Dimitris Manolakis

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.542086

KEYWORDS: Matrices, Statistical analysis, Sensors, Target detection, Linear filtering, Hyperspectral target detection, Hyperspectral imaging, Gaussian filters, Solids, Detection and tracking algorithms

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Proceedings Article | 12 August 2004 Paper

Generation of synthetic HSI data using linear mixing model with Gaussian endmembers

David Stein, Dimitris Manolakis

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.542248

KEYWORDS: Data modeling, Sensors, Statistical modeling, Statistical analysis, Atmospheric modeling, Linear filtering, Hyperspectral imaging, Reflectivity, Performance modeling, Atmospheric sensing

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Proceedings Article | 12 August 2004 Paper

Using elliptically contoured distributions to model hyperspectral imaging data and generate statistically similar synthetic data

David Marden, Dimitris Manolakis

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.542077

KEYWORDS: Data modeling, Mahalanobis distance, Sensors, Statistical modeling, Statistical analysis, Hyperspectral imaging, Algorithm development, Detection and tracking algorithms, Roads, Reflectivity

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Proceedings Article | 23 September 2003 Paper

Modeling hyperspectral imaging data

David Marden, Dimitris Manolakis

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.485933

KEYWORDS: Expectation maximization algorithms, Data modeling, Mahalanobis distance, Scanning electron microscopy, Hyperspectral imaging, Algorithm development, Statistical modeling, Statistical analysis, Sensors, Stochastic processes

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Developing proper models for hyperspectral imaging (HSI) data allows for useful and reliable algorithms for data exploitation. These models provide the foundation for development and evaluation of detection, classification, clustering, and estimation algorithms. To date, real world HSI data has been modeled as a single multivariate Gaussian, however it is well known that real data often exhibits non-Gaussian behavior with multi-modal distributions. Instead of the single multivariate Gaussian distribution, HSI data can be model as a finite mixture model, where each of the mixture components need not be Gaussian. This paper will focus on techniques used to segment HSI data into homogenous clusters. Once the data has been segmented, each individual cluster can be modeled, and the benefits provided by the homogeneous clustering of the data versus non-clustering explored. One of the promising techniques uses the Expectation-Maximization (EM) algorithm to cluster the data into Elliptically Contoured Distributions (ECDs). A larger family of distributions, the family of ECDs includes the mutlivariate Gaussian distribution and exhibits most of its properties. ECDs are uniquely defined by their multivariate mean, covariance and the distribution of its Mahalanobis (or quadratic) distance metric. This metric lets multivariate data be identified using a univariate statistic and can be adjusted to more closely match the longer tailed distributions of real data. This paper will focus on three issues. First, the definition of the multivariate Elliptically Contoured Distribution mixture model will be developed. Second, various techniques will be described that segment the mixed data into homogeneous clusters. Most of this work will focus on the EM algorithm and the multivariate t-distribution, which is a member of the family of ECDs and provides longer tailed distributions than the Gaussian. Lastly, results using HSI data from the AVIRIS sensor will be shown, and the benefits of clustered data will be presented.

Proceedings Article | 8 November 2002 Paper

Constrained detectors for hyperspectral remote sensing applications: theory versus practice

David Marden, Dimitris Manolakis

Proceedings Volume 4816, (2002) https://doi.org/10.1117/12.451563

KEYWORDS: Sensors, Hyperspectral imaging, Remote sensing, Target detection, Data modeling, Error analysis, Binary data, Detection and tracking algorithms, Signal to noise ratio, Interference (communication)

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Proceedings Article | 2 August 2002 Paper

Overview of algorithms for hyperspectral target detection: theory and practice

Dimitris Manolakis

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478752

KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Expectation maximization algorithms, Hyperspectral target detection, Algorithm development, Statistical analysis, Data modeling, Reflectivity, Visibility

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Proceedings Article | 17 January 2002 Paper

Directionally constrained or constrained energy minimization adaptive matched filter: theory and practice

Dimitris Manolakis, Gary Shaw

Proceedings Volume 4480, (2002) https://doi.org/10.1117/12.453327

KEYWORDS: Statistical analysis, Sensors, Detection and tracking algorithms, Filtering (signal processing), Digital filtering, Target detection, Signal detection, Hyperspectral target detection, Algorithm development, Linear filtering

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Proceedings Article | 20 August 2001 Paper

Hyperspectral adaptive matched-filter detectors: practical performance comparison

Dimitris Manolakis, Christina Siracusa, David Marden, Gary Shaw

Proceedings Volume 4381, (2001) https://doi.org/10.1117/12.437006

KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Algorithm development, Data modeling, Antimony, Expectation maximization algorithms, Signal detection, Statistical analysis, Gaussian filters

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Proceedings Article | 20 August 2001 Paper

Statistics of hyperspectral imaging data

Dimitris Manolakis, David Marden, John Kerekes, Gary Shaw

Proceedings Volume 4381, (2001) https://doi.org/10.1117/12.437021

KEYWORDS: Statistical analysis, Data modeling, Detection and tracking algorithms, Sensors, Mahalanobis distance, Target detection, Hyperspectral imaging, Algorithm development, Hyperspectral target detection, Statistical modeling

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Proceedings Article | 23 August 2000 Paper

Comparative analysis of hyperspectral adaptive matched filter detectors

Dimitris Manolakis, Gary Shaw, Nirmal Keshava

Proceedings Volume 4049, (2000) https://doi.org/10.1117/12.410332

KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Digital filtering, Antimony, Hyperspectral target detection, Statistical analysis, Algorithm development, Picosecond phenomena, Algorithms

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Proceedings Article | 23 August 2000 Paper

Algorithm taxonomy for hyperspectral unmixing

Nirmal Keshava, John Kerekes, Dimitris Manolakis, Gary Shaw

Proceedings Volume 4049, (2000) https://doi.org/10.1117/12.410362

KEYWORDS: Taxonomy, Data modeling, Signal to noise ratio, Interference (communication), Error analysis, Algorithm development, Statistical modeling, Detection and tracking algorithms, Dimension reduction, Atmospheric modeling

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Proceedings Article | 16 September 1994 Paper

Fractal-based modeling and interpolation of non-Gaussian images

Stephen Kogon, Dimitris Manolakis

Proceedings Volume 2308, (1994) https://doi.org/10.1117/12.185989

KEYWORDS: Motion models, Data modeling, Stochastic processes, Fractal analysis, Statistical modeling, Statistical analysis, Thermal modeling, Process modeling, Infrared imaging, Infrared radiation

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Proceedings Article | 1 November 1992 Paper

Enhancement of digital angiographic images with misregistration correction and spatially adaptive matched filtering

V. Digalakis, Dimitris Manolakis, P. Lazaridis, Vinay Ingle

Proceedings Volume 1818, (1992) https://doi.org/10.1117/12.131397

KEYWORDS: Digital filtering, Image filtering, Image processing, Angiography, Visual communications, Arteries, Algorithm development, Image registration, Visualization, Blood circulation

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Proceedings Article | 1 October 1992 Paper

Low-cost automatic contrast enhancement for digital x rays

Dimitris Manolakis, John Orr, An-Loong Kok

Proceedings Volume 1705, (1992) https://doi.org/10.1117/12.138456

KEYWORDS: Radiography, X-rays, Spatial resolution, Diagnostics, Signal processing, Image processing, Visual information processing, Image enhancement, Associative arrays, Digital x-ray imaging

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