Spectral quality metrics for VNIR and SWIR hyperspectral imagery

John P. Kerekes; Su May Hsu

doi:10.1117/12.542192

12 August 2004 Spectral quality metrics for VNIR and SWIR hyperspectral imagery

John P. Kerekes, Su May Hsu

Proceedings Volume 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X; (2004) https://doi.org/10.1117/12.542192
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

John P. Kerekes and Su May Hsu "Spectral quality metrics for VNIR and SWIR hyperspectral imagery", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); https://doi.org/10.1117/12.542192

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