Dr. Adam P. Cisz Profile

Dr. Adam P. Cisz

Student at Rochester Institute of Technology

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Publications (2)

Proceedings Article | 1 June 2005 Paper

A comparative evaluation of spectral quality metrics for hyperspectral imagery

John Kerekes, Adam Cisz, Rulon Simmons

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.605916

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

Performance comparison of hyperspectral target detection algorithms in altitude varying scenes

Adam Cisz, John Schott

Proceedings Volume 5806, (2005) https://doi.org/10.1117/12.603768

KEYWORDS: Detection and tracking algorithms, Target detection, Algorithm development, Roads, Visualization, Hyperspectral target detection, Reflectivity, Sensors, Spatial resolution, Data conversion

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