Dr. Alan P. Schaum Profile

Dr. Alan P. Schaum

Physicist at US Naval Research Lab

SPIE Involvement:

Conference Program Committee | Author

Area of Expertise:

hyperspectral image processing , chromodynamics , decision theory , gas vapor detection , sub-pixel detection

Profile Summary

Alan Schaum earned a Ph.D. in Theoretical Physics from The Johns Hopkins University. He applied information and queueing theory to communication problems at Bell Labs until its breakup in 1983. Since then, he has developed signal and image processing methods for threat detection at the US Naval Research Laboratory. He is a member of Phi Beta Kappa, and a Fellow of both SPIE and the Military Sensing Symposium.

Publications (35)

Proceedings Article | 2 November 2022 Presentation + Paper

Presentation + Paper

Tailoring hyperspectral background models to target detection problems

Alan Schaum

Proceedings Volume 12272, 1227202 (2022) https://doi.org/10.1117/12.2633989

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Opacity, Lawrencium, Statistical analysis, Convolution, Coastal modeling, Spectral models, Atmospheric modeling

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Proceedings Article | 31 May 2022 Presentation + Paper

Presentation + Paper

Sub-pixel detection for disparate target and background probability models

Alan Schaum

Proceedings Volume 12094, 120940P (2022) https://doi.org/10.1117/12.2616295

KEYWORDS: Target detection, Detection and tracking algorithms, Convolution, Sensors, Hyperspectral target detection, Data modeling, Composites, Algorithm development, Statistical modeling, Statistical analysis

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Proceedings Article | 12 April 2021 Presentation + Paper

Presentation + Paper

Lognormal background modeling for gaseous plume detection

Alan Schaum

Proceedings Volume 11727, 117270A (2021) https://doi.org/10.1117/12.2584363

KEYWORDS: Sensors, Radar, Detection and tracking algorithms, Statistical modeling, Spectral models

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Proceedings Article | 14 May 2020 Paper

Paper

Constructing optimal classifiers from sub-optimal composite hypothesis tests

Alan Schaum

Proceedings Volume 11392, 113920W (2020) https://doi.org/10.1117/12.2556105

KEYWORDS: Image segmentation, Sensors, Detection and tracking algorithms, Target detection, Hyperspectral imaging

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Proceedings Article | 8 May 2018 Presentation + Paper

Presentation + Paper

Closed-form solutions to replacement target models of sub-pixel spectral detection

Alan Schaum

Proceedings Volume 10644, 1064406 (2018) https://doi.org/10.1117/12.2303839

KEYWORDS: Target detection, Sensors, Opacity, Detection and tracking algorithms, Spectral models, Coastal modeling, Target recognition, Mathematical modeling, Data modeling, Information fusion

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Proceedings Article | 5 May 2017 Paper

Paper

Invariance concepts in spectral analysis

Alan Schaum

Proceedings Volume 10198, 101980H (2017) https://doi.org/10.1117/12.2260469

KEYWORDS: Sensors, Target detection, Binary data, Analytical research, Lead, Composites, Detection theory, Detection and tracking algorithms, Atmospheric modeling, Data modeling, Environmental sensing, Remote sensing, Hyperspectral imaging, Reflectivity, Aerosols, Statistical analysis, Algorithm development

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Proceedings Article | 18 October 2016 Presentation + Paper

Presentation + Paper

Clairvoyant fusion: a new methodology for designing robust detection algorithms

Alan Schaum

Proceedings Volume 10004, 100040C (2016) https://doi.org/10.1117/12.2240092

KEYWORDS: Binary data, Environmental sensing, Target detection, Detection and tracking algorithms, Sensors, Composites, Neodymium, Electro optical modeling, Statistical modeling, Algorithms

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Proceedings Article | 19 September 2016 Presentation + Paper

Presentation + Paper

Clairvoyant fusion detection of ocean anomalies in WorldView-2 spectral imagery

Alan Schaum, Eric Allman, Matthew Stites

Proceedings Volume 9976, 99760G (2016) https://doi.org/10.1117/12.2239142

KEYWORDS: Satellites, Sensors, Detection and tracking algorithms, Image fusion, Clouds, Stochastic processes, Electro optical modeling, Target detection, Data modeling, Statistical modeling

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Proceedings Article | 13 June 2014 Paper

Paper

Enough with the additive target model

Alan Schaum

Proceedings Volume 9088, 90880C (2014) https://doi.org/10.1117/12.2049146

KEYWORDS: Target detection, Detection and tracking algorithms, Electro optical modeling, Opacity, Signal processing, Signal detection, Californium, Coastal modeling, Statistical modeling, Sensors

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Proceedings Article | 18 May 2013 Paper

Paper

Extending continuum fusion to create unbeatable detectors

Alan Schaum

Proceedings Volume 8743, 87430D (2013) https://doi.org/10.1117/12.2015158

KEYWORDS: Sensors, Composites, Logic, Probability theory, Binary data, Target detection, Ions, Detection and tracking algorithms, Neodymium, Analytical research

Read Abstract +

We develop an extension of continuum fusion methods that allows the generation of unbeatable decision rules for discrete binary composite hypothesis testing problems. Background: Amongst the many flavors of continuum fusion (CF) algorithm, one can always be found that will produce the uniformly most powerful (UMP) solution to any composite hypothesis (CH) testing problem, when such a solution exists [1]. This optimality property, combined with the flexibility in design afforded by CF principles, led to the prospect that with any reasonably defined optimality metric, any detection problem could be solved with some CF-based decision rule (DR). Doubt was cast on this possibility in a paper by Theiler [2], who showed that applying continuum fusion logical rules to a particular discrete (as opposed to continuum) problem could not produce the better algorithm. Theiler’s example requires creation of a CH test, and for these no generally optimal form exists. However, Theiler’s problem also obeyed an invariance principle, and if solutions are restricted to obey the same invariance, then a uniformly most powerful invariant (UMPI) solution does exist. This solution cannot be generated by applying current CF principles to this discrete parameter problem. In short, standard CF logic cannot produce a highly desirable answer. The UMPI solution exemplifies Bayesian solutions to discrete parameter CH problems, and it is shown below why standard CF solutions cannot always produce them, in agreement with Theiler’s result. Bayesian solutions feature prominently in statistical decision theory, because they form the class of unbeatable decision rules, as defined below. Thus, standard CF principles cannot produce an important class of solutions to discrete CH problems. Here we extend the CF methodology in a way that converts any discrete parameter fusion problem into a continuous one. Continuum fusion solutions to the converted problem then generate the entire class of unbeatable detectors.

Proceedings Article | 18 May 2013 Paper

Paper

Automatic ship detection from commercial multispectral satellite imagery

Brian Daniel, Alan Schaum, Eric Allman, Robert Leathers, Trijntje Downes

Proceedings Volume 8743, 874312 (2013) https://doi.org/10.1117/12.2017762

KEYWORDS: Clouds, Multispectral imaging, Sensors, Neodymium, Satellite communications, Satellites, Detection and tracking algorithms, Image fusion, Satellite imaging, Earth observing sensors

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SPIE Journal Paper | 8 August 2012

Continuum fusion methods of spectral detection

Alan Schaum, Brian Daniel

OE, Vol. 51, Issue 11, 111718, (August 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.11.111718

KEYWORDS: Sensors, Target detection, Detection and tracking algorithms, Data modeling, Lawrencium, Affine motion model, Optical engineering, Hyperspectral systems, Hyperspectral target detection, Statistical analysis

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Proceedings Article | 9 May 2012 Paper

Paper

Progress in the theory of continuum fusion

A. Schaum

Proceedings Volume 8390, 839002 (2012) https://doi.org/10.1117/12.917737

KEYWORDS: Sensors, Target detection, Detection and tracking algorithms, Lawrencium, Optical spheres, Composites, Image analysis, Ions, Binary data, Statistical analysis

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Proceedings Article | 20 May 2011 Paper

Paper

Linear log-likelihood ratio (L3R) algorithm for spectral target detection

Brian Daniel, Alan Schaum

Proceedings Volume 8048, 804804 (2011) https://doi.org/10.1117/12.884337

KEYWORDS: Detection and tracking algorithms, Target detection, RGB color model, Linear filtering, Human-machine interfaces, Hyperspectral imaging, Palladium, Analytical research, Gaussian filters, Hyperspectral target detection

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Proceedings Article | 20 May 2011 Paper

Paper

Design methods for continuum fusion detectors

A. Schaum

Proceedings Volume 8048, 804803 (2011) https://doi.org/10.1117/12.883497

KEYWORDS: Sensors, Detection and tracking algorithms, Target detection, Algorithm development, Logic, Data modeling, Visualization, Composites, Lawrencium, Affine motion model

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Proceedings Article | 12 May 2011 Paper

Paper

The continuum fusion theory of signal detection applied to a bi-modal fusion problem

A. Schaum

Proceedings Volume 8064, 806403 (2011) https://doi.org/10.1117/12.883491

KEYWORDS: Detection and tracking algorithms, Sensors, Algorithm development, Signal detection, Target detection, Signal processing, Composites, Logic, Detector development, Electro optical modeling

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Proceedings Article | 11 May 2011 Paper

Paper

Next generation signature-based hyperspectral detection: a challenge to atmospheric modelers

A. Schaum, Brian Daniel

Proceedings Volume 8040, 80400C (2011) https://doi.org/10.1117/12.883506

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Atmospheric modeling, Hyperspectral target detection, Atmospheric sensing, Algorithm development, Visualization, Reflectivity, Systems modeling

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Proceedings Article | 13 May 2010 Paper

Paper

Urchin: an RX-derivative accounting for anisotropies in whitened clutter

Brian Daniel, Alan Schaum

Proceedings Volume 7695, 769504 (2010) https://doi.org/10.1117/12.850222

KEYWORDS: Anisotropy, Target detection, Detection and tracking algorithms, Hyperspectral imaging, Data modeling, Mahalanobis distance, Palladium, Analytical research, Detection theory, Spherical lenses

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Proceedings Article | 13 May 2010 Paper

Paper

Continuum fusion: a new methodology for creating hyperspectral detection algorithms

A. Schaum

Proceedings Volume 7695, 769502 (2010) https://doi.org/10.1117/12.849475

KEYWORDS: Detection and tracking algorithms, Sensors, Algorithm development, Target detection, Composites, Logic, Data modeling, Atmospheric modeling, Lawrencium, Detector development

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Proceedings Article | 27 April 2009 Paper

Paper

The affine matched filter

A. Schaum, Richard Priest

Proceedings Volume 7334, 733403 (2009) https://doi.org/10.1117/12.817409

KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Reflectivity, Calibration, Chromium, Algorithm development, Short wave infrared radiation, Atmospheric corrections, Atmospheric modeling

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Proceedings Article | 27 April 2009 Paper

Paper

A hyperspectral anomaly detector based on partialing out a clutter subspace

Edisanter Lo, Alan Schaum

Proceedings Volume 7334, 733404 (2009) https://doi.org/10.1117/12.821012

KEYWORDS: Sensors, Hyperspectral imaging, Mahalanobis distance, Detection and tracking algorithms, Target detection, Analytical research, Image sensors, Statistical analysis, Coastal modeling, Hyperspectral target detection

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Proceedings Article | 14 May 2007 Paper

Paper

Hyperspectral anomaly detection beyond RX

A. Schaum

Proceedings Volume 6565, 656502 (2007) https://doi.org/10.1117/12.718789

KEYWORDS: Sensors, Detection and tracking algorithms, Target detection, Hyperspectral imaging, Lawrencium, Anisotropy, Spherical lenses, Algorithm development, Statistical modeling, Machine learning

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Proceedings Article | 7 May 2007 Paper

Paper

Hyperspectral change detection in high clutter using elliptically contoured distributions

A. Schaum, Eric Allman, John Kershenstein, Drew Alexa

Proceedings Volume 6565, 656515 (2007) https://doi.org/10.1117/12.729632

KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Hyperspectral target detection, Environmental sensing, Remote sensing, Optimization (mathematics), Signal detection, Filtering (signal processing), Mahalanobis distance

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Proceedings Article | 4 May 2006 Paper

Paper

Signature evolution with covariance equalization in oblique hyperspectral imagery

Robert Leathers, Alan Schaum, Trijntje Downes

Proceedings Volume 6233, 623307 (2006) https://doi.org/10.1117/12.669136

KEYWORDS: Sensors, Short wave infrared radiation, Atmospheric sensing, Hyperspectral imaging, Reflectivity, Remote sensing, Detection and tracking algorithms, Atmospheric optics, Time metrology, Cameras

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Proceedings Article | 4 May 2006 Paper

Paper

Hyperspectral detection algorithms: from old ideas to operational concepts to next generation

A. Schaum

Proceedings Volume 6233, 623305 (2006) https://doi.org/10.1117/12.665754

KEYWORDS: Detection and tracking algorithms, Sensors, Target detection, Systems modeling, Matrices, Analytical research, Hyperspectral target detection, Hyperspectral imaging, Signal processing, Statistical analysis

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Proceedings Article | 12 August 2004 Paper

Paper

Hyperspectral change detection and supervised matched filtering based on covariance equalization

Alan Schaum, Alan Stocker

Proceedings Volume 5425, (2004) https://doi.org/10.1117/12.544026

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Hyperspectral target detection, Filtering (signal processing), Error analysis, Signal processing, Data processing, Image processing, Human-machine interfaces

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Proceedings Article | 23 September 2003 Paper

Paper

Hyperspectral multipass mapping for target detection

Alan Schaum, Alan Stocker

Proceedings Volume 5093, (2003) https://doi.org/10.1117/12.488307

KEYWORDS: Error analysis, Target detection, Sensors, Matrices, Detection and tracking algorithms, Image registration, Long wavelength infrared, Filtering (signal processing), Transform theory, Statistical analysis

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Proceedings Article | 2 August 2002 Paper

Paper

Joint hyperspectral subspace detection derived from a Bayesian likelihood ratio test

Alan Schaum, Alan Stocker

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478754

KEYWORDS: Target detection, Sensors, Hyperspectral target detection, Detection and tracking algorithms, Hyperspectral imaging, Lawrencium, Statistical analysis, Gaussian filters, Interference (communication), Infrared signatures

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Proceedings Article | 20 August 2001 Paper

Paper

Spectral subspace matched filtering

Alan Schaum

Proceedings Volume 4381, (2001) https://doi.org/10.1117/12.436996

KEYWORDS: Target detection, Sensors, Detection and tracking algorithms, Linear filtering, Lawrencium, Optical filters, Statistical analysis, Remote sensing, Image processing, Mathematical modeling

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Proceedings Article | 23 August 2000 Paper

Paper

Target detection enhancement using temporal signature propagation

Rulon Mayer, Alan Schaum

Proceedings Volume 4049, (2000) https://doi.org/10.1117/12.410372

KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Optical filters, Atmospheric sensing, Atmospheric modeling, Atmospheric propagation, Hyperspectral target detection, Atmospheric corrections, Data acquisition

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Proceedings Article | 4 August 1997 Paper

Paper

Application of stochastic mixing models to hyperspectral detection problems

Alan Stocker, Alan Schaum

Proceedings Volume 3071, (1997) https://doi.org/10.1117/12.280584

KEYWORDS: Target detection, Data modeling, Stochastic processes, Sensors, Hyperspectral target detection, Scanning electron microscopy, Vegetation, Hyperspectral imaging, Statistical analysis, Binary data

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Proceedings Article | 4 August 1997 Paper

Paper

Subclutter target detection using sequences of thermal infrared multispectral imagery

Alan Schaum, Alan Stocker

Proceedings Volume 3071, (1997) https://doi.org/10.1117/12.280605

KEYWORDS: Target detection, Infrared imaging, Sensors, Infrared radiation, Long wavelength infrared, Thermography, Spectroscopy, Detection and tracking algorithms, Infrared detectors, Multispectral imaging

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Proceedings Article | 1 September 1995 Paper

Paper

Target detection in desert backgrounds: infrared hyperspectral measurements and analysis

Michael Eismann, John Seldin, Craig Schwartz, James Maxwell, Kenneth Ellis, Jack Cederquist, Alan Stocker, Ara Oshagan, Ray Johnson, William Shaffer, Marc Surette, Martin McHugh, Alan Schaum, Larry Stotts

Proceedings Volume 2561, (1995) https://doi.org/10.1117/12.217672

KEYWORDS: Target detection, Infrared radiation, Fourier transforms, Sensors, Black bodies, Infrared sensors, Infrared spectroscopy, Mid-IR, Infrared signatures, Infrared detectors

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Proceedings Article | 2 June 1995 Paper

Paper

Analysis of infrared hyperspectral measurements by the joint multispectral program

Alan Stocker, Ara Oshagan, William Shaffer, Marc Surette, Martin McHugh, Alan Schaum, Michael Eismann, Kenneth Ellis, Robert Maxwell, John Seldin

Proceedings Volume 2469, (1995) https://doi.org/10.1117/12.210626

KEYWORDS: Target detection, Long wavelength infrared, Mid-IR, Temperature metrology, Fourier transforms, Reflectivity, Environmental sensing, Infrared radiation, Sensors, Vegetation

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Proceedings Article | 25 February 1994 Paper

Paper

Optimal change detection in misregistered image sequences

Alan Schaum

Proceedings Volume 2103, (1994) https://doi.org/10.1117/12.169473

KEYWORDS: Image filtering, Digital imaging, Sensors, Image processing, Digital filtering, Filtering (signal processing), Electro optics, Image sensors, Linear filtering, Optical filters

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Showing 5 of 35 publications

Conference Committee Involvement (23)

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXXI

15 April 2025 | Orlando, Florida, United States

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

23 April 2024 | National Harbor, Maryland, United States

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

2 May 2023 | Orlando, Florida, United States

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII

5 April 2022 | Orlando, Florida, United States

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII

12 April 2021 | Online Only, Florida, United States

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVI

27 April 2020 | Online Only, California, United States

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV

16 April 2019 | Baltimore, MD, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV

16 April 2018 | Orlando, FL, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII

11 April 2017 | Anaheim, CA, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII

18 April 2016 | Baltimore, MD, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI

21 April 2015 | Baltimore, MD, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX

5 May 2014 | Baltimore, MD, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX

29 April 2013 | Baltimore, Maryland, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII

23 April 2012 | Baltimore, Maryland, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII

25 April 2011 | Orlando, Florida, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI

5 April 2010 | Orlando, Florida, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV

13 April 2009 | Orlando, Florida, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV

17 March 2008 | Orlando, Florida, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII

9 April 2007 | Orlando, Florida, United States

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

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

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI

28 March 2005 | Orlando, Florida, United States

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

12 April 2004 | Orlando, Florida, United States

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX

21 April 2003 | Orlando, Florida, United States

Showing 5 of 23 Conference Committees

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