Mr. Rulon E. Simmons Profile

Rulon E. Simmons

Spectral Studies Manager at ITT Industries

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Publications (7)

Proceedings Article | 4 May 2006 Paper

Comparisons between spectral quality metrics and analyst performance in hyperspectral target detection

John Kerekes, David Messinger, Paul Lee, Rulon Simmons

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

KEYWORDS: Image quality, Image analysis, Image processing, Signal to noise ratio, Hyperspectral imaging, Spatial resolution, Target detection, Sensors, Image filtering, Image resolution

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Proceedings Article | 1 June 2005 Paper

A comparative evaluation of spectral quality metrics for hyperspectral imagery

John Kerekes, Adam Cisz, Rulon Simmons

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

KEYWORDS: Image quality, Signal to noise ratio, Image analysis, Target detection, Hyperspectral imaging, Sensors, Image fusion, Image sensors, Spatial resolution, Palladium

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Quantitative methods to assess or predict the quality of a spectral image are the subject of a number of current research activities. An accepted methodology would be highly desirable to use for data collection tasking or data archive searches in way analogous to the current uses of the National Imagery Interpretation Rating Scale (NIIRS) General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published. An issue with many of these approaches is that they tend to be constructed around specific tasks (target detection, background classification, etc.) While this has often been necessary to make the quality assessment tractable, it is desirable to have a method that is more general. One such general approach is presented in a companion paper (Simmons, et al). This new approach seeks to get at the heart of the general spectral imagery quality analysis problem−assessing the confidence of an image analyst in performing a specified task with a specific spectral image. In this approach the quality from spatial and spectral aspects of the imagery are treated separately and then a fusion concept known as “semantic transformation” is used to combine the utility, or confidence, from these two aspects into an overall quality metric. This paper compares and contrasts the various methods published in the literature with this new General Spectral Utility Metric (GSUM). In particular, the methods are applied to a target detection problem using data from the airborne HYDICE instrument collected at Forest Radiance I. While the GSUM approach is seen to lead to intuitively pleasing results, its sensitivity to image parameters was not seen to be consistent with previously published approaches. However, this likely resulted more from limitations of the previous approaches than with problems with GSUM. Further studies with additional spectral imaging applications are recommended along with efforts to integrate a performance predication capability into the GSUM framework.

Proceedings Article | 1 June 2005 Paper

General spectral utility metric for spectral imagery

Rulon Simmons, Timothy Elder, David Stewart, Eric Cincotta, Carolyn Kennedy, R. Craig Van Nostrand

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

KEYWORDS: Target detection, Image analysis, Signal to noise ratio, Information visualization, Visualization, Target recognition, Image quality, Optical filters, Detection and tracking algorithms, Remote sensing

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Published approaches to assessing and predicting spectral image utility are generally based on regression methods which fit coefficients to an equation with terms representing spatial scale, spectral fidelity, and signal-to-noise. Such approaches are patterned after the National Imagery Interpretability Rating Scale General Image Quality Equation (NIIRS GIQE) designed for use with remotely-sensed panchromatic imagery. Preliminary testing of these approaches suggests that they will work for some subsets of spectral imagery applications but are not generally applicable to all spectral imaging problems. We present here an approach that gets at the heart of the general problem−assessing the confidence of an image analyst in performing a specified task with a specific spectral image. While applicable in other areas such as health imaging, our approach to spectral utility assessment is presented in this paper from a remote sensing point of view. Our approach allows trade-offs in tasking and system design across the “spectrum” of imagers including panchromatic, multispectral, hyperspectral, and even ultraspectral. Our approach is based on a fusion concept called “semantic transformation.” We assume that spectral and spatial information are largely separable with both contributing to the overall utility of the image. The “semantic transformation” combines the spatial and spectral information in a common term (in our case confidence) to give an overall confidence in performing the specified task. Addressing the spatial and spectral information separately allows us the freedom to assess the information contained in each in ways that the information is actually assimilated (i.e., usually spatial information in exploited visually while spectral information consisting of more than three or four bands is usually exploited by computer algorithms). For the spectral information, we can use either generic exploitation algorithms or the specific algorithms that the image analyst would be expected to use. Testing of our approach was done with a parametric set of simulated imagery where Ground Sampled Distance (GSD) and the number of spectral bands were varied. Our initial test led to some refinements of our approach, which are discussed.

Proceedings Article | 12 May 2005 Paper

A mechanism for the management and optimization of imaging systems with non-uniform imaging quality

Thomas Sanderson, Paul Sprague, Steven Smith, Rulon Simmons, Raymond Lines

Proc. SPIE. 5784, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVI

KEYWORDS: Image quality, Sensors, Cameras, Imaging systems, Data modeling, Image processing, Image sensors, Sun, Remote sensing, Unmanned aerial vehicles

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Proceedings Article | 16 October 1998 Paper

Thermal infrared hyperspectral data compression

Bernard Brower, Austin Lan, Rulon Simmons, David Haddock

Proc. SPIE. 3438, Imaging Spectrometry IV

KEYWORDS: Image segmentation, Image compression, Thermography, Infrared imaging, Infrared radiation, Data compression, Algorithm development, Sensors, Black bodies, Wavelets

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Proceedings Article | 19 September 1997 Paper

Spectral feature enhancement for hyperspectral imagery

Austin Lan, Rulon Simmons, Bernard Brower, Joseph Reitz

Proc. SPIE. 3119, Multispectral Imaging for Terrestrial Applications II

KEYWORDS: Sensors, Optical filters, Convolution, Signal to noise ratio, Hyperspectral imaging, Image filtering, Digital filtering, Databases, Absorption, Detection and tracking algorithms

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Proceedings Article | 19 September 1997 Paper

Data characterization for hyperspectral image compression

Rulon Simmons, Bernard Brower, John Schott

Proc. SPIE. 3119, Multispectral Imaging for Terrestrial Applications II

KEYWORDS: Sensors, Reflectivity, Matrices, Algorithm development, Hyperspectral imaging, Absorption, Imaging systems, Data compression, Atmospheric sensing, Interference (communication)

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Showing 5 of 7 publications

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