Current image quality approaches are designed to assess the utility of single band images by trained image analysts. While analysts today are certainly involved in the exploitation of spectral imagery, automated tools are generally used as aids in the analysis and offer hope in the future of significantly reducing the timeline and analysis load. Thus, there is a recognized need for spectral image quality metrics that include the effects of automated algorithms. We have begun initial efforts in this area through the use of a parametric modeling tool to gain insight into parameter dependence on system performance in unresolved object detection applications. An initial Spectral Quality Equation (SQE) has been modeled after the National Imagery Interpretation Rating Scale General Image Quality Equation (NIIRS GIQE). The parameter sensitivities revealed through the model-based trade studies were assessed through comparison to analogous studies conducted with available data. This current comparison has focused on detection applications using sensors operating in the VNIR and SWIR spectral regions. The SQE is shown with key image parameters and sample coefficients. Results derived from both model-based trade studies and empirical data analyses are compared. Extensions of the SQE approach to additional application areas such as material identification and terrain classification are also discussed.

Date of creation, presentation, or exhibit



Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X 5425 (2004) 549-557 "Spectral quality metrics for VNIR and SWIR hyperspectral imagery," Proceedings of the Defense Security Symposium. International Society of Optical Engineers. Held at Gaylord Palms Resort: Orlando, Florida: 12-16 April 2004. Copyright 2004 Society of Photo-Optical Instrumentation Engineers. This paper was published in Proceedings of Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE vol. 5806 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The authors acknowledge support for this work by Mr. Ernie Reith and Mr. Wayne Hallada of the National Geospatial- Intelligence Agency. The authors would like to thank Capt. Paul Millhouse (USAF) for his thoughtful discussions on spectral quality metrics. Our colleague Kris Farrar (MIT/LL) is gratefully acknowledged for having performed the model runs used in this work. The HYDICE data used in the empirical analyses were supplied by the Spectral Information Technology Applications Center. ISSN: 0277-786X Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


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