Dr. Scott G. Beaven Profile

Dr. Scott G. Beaven

Engineer at L3Harris Technologies Inc

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Author

Publications (17)

Proceedings Article | 13 June 2014 Paper

Multisensor data fusion across time and space

Pierre Villeneuve, Scott Beaven, Robert Reed

Proceedings Volume 9088, 90880S (2014) https://doi.org/10.1117/12.2050733

KEYWORDS: Sensors, Image sensors, Optical flow, Image processing, Image registration, Long wavelength infrared, RGB color model, Spatial resolution, Data processing, Data fusion

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Proceedings Article | 24 September 2013 Paper

Hyperspectral data compression using a Wiener filter predictor

Pierre Villeneuve, Scott Beaven, Alan Stocker

Proceedings Volume 8871, 887102 (2013) https://doi.org/10.1117/12.2024629

KEYWORDS: Image compression, Data modeling, Data compression, Sensors, Filtering (signal processing), Detection and tracking algorithms, Algorithm development, Computer programming, Hyperspectral imaging, Image processing

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Proceedings Article | 4 June 2013 Paper

Precision radiometric surface temperature (PRST) sensor

James Daly, Carson Roberts, Andrew Bodkin, Robert Sundberg, Scott Beaven, Jeffrey Weinheimer

Proceedings Volume 8707, 87070A (2013) https://doi.org/10.1117/12.2016092

KEYWORDS: Temperature metrology, Sensors, Imaging systems, Hyperspectral imaging, Mid-IR, Databases, Cameras, Aluminum, Black bodies, Pyrometry

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Proceedings Article | 18 May 2012 Paper

Video-rate chemical identification and visualization with snapshot hyperspectral imaging

Andrew Bodkin, A. Sheinis, A. Norton, J. Daly, C. Roberts, S. Beaven, J. Weinheimer

Proceedings Volume 8374, 83740C (2012) https://doi.org/10.1117/12.919202

KEYWORDS: Imaging systems, Hyperspectral imaging, Video, Mid-IR, Long wavelength infrared, Black bodies, Staring arrays, Spatial resolution, Calibration, Visible radiation

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Proceedings Article | 17 September 2011 Paper

Spectral DAISY: a combined target spatial-spectral dense feature descriptor for improved tracking performance

Jeffrey Weinheimer, Pierre Villeneuve, Scott Beaven

Proceedings Volume 8137, 813706 (2011) https://doi.org/10.1117/12.892816

KEYWORDS: Spatial resolution, RGB color model, Sensors, Image processing, Target detection, Convolution, Detection and tracking algorithms, Kinematics, Spectral data processing, Hyperspectral target detection

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Proceedings Article | 10 September 2009 Paper

Resolution enhancement of hyperspectral data using multispectral imagery

Michael Winter, Edwin Winter, Scott Beaven

Proceedings Volume 7473, 74730F (2009) https://doi.org/10.1117/12.830231

KEYWORDS: Hyperspectral imaging, Multispectral imaging, Image filtering, Vegetation, Spatial resolution, Image resolution, Sensors, Image processing, Imaging systems, Linear filtering

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Proceedings Article | 27 April 2009 Paper

Snapshot hyperspectral imaging: the hyperpixel array camera

Andrew Bodkin, A. Sheinis, A. Norton, J. Daly, S. Beaven, J. Weinheimer

Proceedings Volume 7334, 73340H (2009) https://doi.org/10.1117/12.818929

KEYWORDS: Imaging systems, Long wavelength infrared, Mid-IR, Staring arrays, Hyperspectral imaging, Black bodies, Nonuniformity corrections, Visible radiation, Prisms, Calibration

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Proceedings Article | 30 October 2007 Paper

Resolution enhancement of Hyperion hyperspectral data using Ikonos multispectral data

Edwin Winter, Michael Winter, Scott Beaven, Anthony Ratkowski

Proceedings Volume 6749, 67490P (2007) https://doi.org/10.1117/12.740682

KEYWORDS: Hyperspectral imaging, Earth observing sensors, High resolution satellite images, Multispectral imaging, Vegetation, Sensors, Spatial resolution, Reflectivity, Data acquisition, Image registration

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We have developed a new and innovative technique for combining a high-spatial-resolution multispectral image with a lower-spatial-resolution hyperspectral image. The approach, called CRISP, compares the spectral information present in the multispectral image to the spectral content in the hyperspectral image and derives a set of equations to approximately transform the multispectral image into a synthetic hyperspectral image. This synthetic hyperspectral image is then recombined with the original low-spatial-resolution hyperspectral image to produce a sharpened product. The result is a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the multispectral image. To test the accuracy of the CRISP method, we applied the method to synthetic data generated from hyperspectral images acquired with an airborne sensor. These high-spatial-resolution images were used to generate both a lower-spatial-resolution hyperspectral data set and a four-band multispectral data set. With this method, it is possible to compare the output of the CRISP process to the 'truth data' (the original scene). In all of these controlled tests, the CRISP product showed both good spectral and visual fidelity, with an RMS error less than one percent when compared to the 'truth' image. We then applied the method to real world imagery collected by the Hyperion sensor on EO-1 as part of the Hurricane Katrina support effort. In addition to multiple Hyperion data sets, both Ikonos and QuickBird data were also acquired over the New Orleans area. Following registration of the data sets, multiple high-spatial-resolution CRISP-generated hyperspectral data sets were created. In this paper, we present the results of this study that shows the utility of the CRISP-sharpened products to form material classification maps at four-meter resolution from space-based hyperspectral data. These products are compared to the equivalent products generated from the source 30m resolution Hyperion data.

Proceedings Article | 4 May 2006 Paper

High-performance fusion of multispectral and hyperspectral data

Michael Winter, Edwin Winter, Scott Beaven, Anthony Ratkowski

Proceedings Volume 6233, 62331R (2006) https://doi.org/10.1117/12.668622

KEYWORDS: Hyperspectral imaging, Multispectral imaging, Spatial resolution, Data modeling, Image processing, Sensors, Landsat, Algorithm development, Earth observing sensors, High resolution satellite images

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Proceedings Article | 1 June 2005 Paper

Hyperspectral image sharpening using multispectral data

Michael Winter, Edwin Winter, Scott Beaven, Anthony Ratkowski

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.606054

KEYWORDS: Hyperspectral imaging, Sensors, Spatial resolution, Image filtering, Target detection, Multispectral imaging, Vegetation, Imaging systems, Image processing, Data modeling

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Proceedings Article | 21 September 2004 Paper

Joint multisensor exploitation for mine detection

Scott Beaven, Alan Stocker, Edwin Winter

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.544843

KEYWORDS: Sensors, Land mines, Mid-IR, Statistical modeling, Image processing, Image registration, Target detection, Detection and tracking algorithms, Optical filters, Imaging systems

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Proceedings Article | 2 August 2002 Paper

Automated Gaussian spectral clustering of hyperspectral data

Scott Beaven, Geoffrey Hazel, Alan Stocker

Proceedings Volume 4725, (2002) https://doi.org/10.1117/12.478758

KEYWORDS: Data modeling, Sensors, Image segmentation, Synthetic aperture radar, Statistical analysis, Hyperspectral imaging, Target detection, Detection and tracking algorithms, Statistical modeling, Backscatter

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Proceedings Article | 15 November 2000 Paper

Reduced dimension quadratic detection algorithms

David Stein, Scott Beaven, Stephen Stewart

Proceedings Volume 4132, (2000) https://doi.org/10.1117/12.406581

KEYWORDS: Signal to noise ratio, Transform theory, Signal attenuation, Detection and tracking algorithms, Quantization, Signal detection, Sensors, Optical filters, Hyperspectral imaging, Image filtering

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Proceedings Article | 27 October 1999 Paper

Effect of sensor defects on hyperspectral mixture analysis algorithms

Edwin Winter, Scott Beaven

Proceedings Volume 3753, (1999) https://doi.org/10.1117/12.366278

KEYWORDS: Sensors, Scanning electron microscopy, Expectation maximization algorithms, Stochastic processes, Calibration, Data modeling, Image segmentation, Spectroscopy, Detection and tracking algorithms, Image sensors

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Proceedings Article | 27 October 1999 Paper

Comparison of SEM and linear unmixing approaches for classification of spectral data

Scott Beaven, Lawrence Hoff, Edwin Winter

Proceedings Volume 3753, (1999) https://doi.org/10.1117/12.366292

KEYWORDS: Scanning electron microscopy, Stochastic processes, Mathematical modeling, Data modeling, Expectation maximization algorithms, Statistical analysis, Statistical modeling, Sensors, Multispectral imaging, Image processing

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Proceedings Article | 29 October 1997 Paper

Generalized linear feature detection of weak targets in spectrally mixed clutter

Xiaoli Yu, Lawrence Hoff, Scott Beaven, Edwin Winter, John Antoniades, Irving Reed

Proceedings Volume 3163, (1997) https://doi.org/10.1117/12.283957

KEYWORDS: Target detection, Sensors, Algorithm development, Detection and tracking algorithms, Buildings, Signal to noise ratio, Data fusion, Infrared imaging, Infrared radiation, Silicon

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Proceedings Article | 31 May 1996 Paper

Analysis of hyperspectral infrared and low-frequency SAR data for target classification

Scott Beaven, Xiaoli Yu, Lawrence Hoff, An Mei Chen, Edwin Winter

Proceedings Volume 2759, (1996) https://doi.org/10.1117/12.241162

KEYWORDS: Synthetic aperture radar, Sensors, Infrared imaging, Infrared radiation, Target detection, Infrared sensors, Hyperspectral target detection, Data fusion, Target recognition, Hyperspectral imaging

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Showing 5 of 17 publications

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