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Proceedings Paper

A microscene approach to the evaluation of hyperspectral system level performance
Author(s): David W. Allen; Ronald G. Resmini; Christopher J. Deloye; Jeffrey R. Stevens
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Paper Abstract

Assessing the ability of a hyperspectral imaging (HSI) system to detect the presence of a substance or to quantify abundance requires an understanding of the many factors in the end-to-end remote sensing scenario from scene to sensor to data exploitation. While there are methods which attempt to model such an overall scenario, they are necessarily implemented with assumptions and approximations that do not completely capture the true complexity of the actual radiative transfer processes nor do they capture the range of variability that materials display in a natural setting. We propose one alternative to numerical data models that generate hyperspectral image cubes for system trade studies and for algorithm development and testing. This approach makes use of compact hyperspectral imagers that can be used in the laboratory to measure materials in a 'microscene' specific to one’s application. The key to acceptance of this approach is quantifying the distributions of spectra as points in n-D space so that one can compare the spectral complexity of laboratory generated microscene data to that of an earth remote sensing scene. The spectral complexity of the microscene generated in the lab is thus compared to airborne remotely sensed HSI. We produce and measure a microscene, estimate its data dimensionality, and compare that to similar estimates of dimensionality of airborne HSI data sets. Signal-to-clutter ratios (SCR) of the microscene are also compared to those derived from airborne HSI data. The results suggest the microscene is capable of producing a scene that is as complex, if not more so, than that of a hyperspectral scene collected from an airborne sensor. A scene classification analysis and a system trade study are conducted to illustrate the utility of the microscene for assessing system-level performance. This simple, low-cost method can provide proxy data with a distribution of points in n-dimensional (n-D) hyperspace that are indistinguishable from an earth remote sensing scene.

Paper Details

Date Published: 18 May 2013
PDF: 13 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431M (18 May 2013); doi: 10.1117/12.2015834
Show Author Affiliations
David W. Allen, National Institute of Standards and Technology (United States)
Ronald G. Resmini, The MITRE Corp. (United States)
Christopher J. Deloye, The MITRE Corp. (United States)
Jeffrey R. Stevens, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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