Article: Further extensions to robust parameter design: three factor interactions with an application to hyperspectral imagery Journal: International Journal of Quality Engineering and Technology (IJQET) 2013 Vol.3 No.3 pp.204 - 218 Abstract: Hyperspectral imagery (HSI) provides opportunities for locating anomalous objects through the use of multivariate statistics. Global anomaly detectors, such as the autonomous global anomaly detector (AutoGAD), require the user to provide various parameters/thresholds to analyse an image. These user-defined settings can be thought of as control variables and properties of the imagery can be employed as noise variables. The presence of these factors suggests the use of robust parameter design (RPD) to locate the best settings for the algorithm. Mindrup et al. (2012) showed that the standard RPD model might not be sufficient for use with more complex data and extended the model to include noise by noise interactions. This paper extends the model to include control by noise by noise and noise by control by control interactions. These new models are then applied to AutoGAD output and the Lin and Tu MSE method is employed to locate optimal settings. Inderscience Publishers - linking academia, business and industry through research

Title: Further extensions to robust parameter design: three factor interactions with an application to hyperspectral imagery

Authors: Jason P. Williams; Kenneth W. Bauer; Mark A. Friend

Addresses: Department of Operational Sciences, Air Force Institute of Technology, 2950 Hobson Way, WPAFB, OH 45433, USA ' Department of Operational Sciences, Air Force Institute of Technology, 2950 Hobson Way, WPAFB, OH 45433, USA ' Department of Operational Sciences, Air Force Institute of Technology, 2950 Hobson Way, WPAFB, OH 45433, USA

Abstract: Hyperspectral imagery (HSI) provides opportunities for locating anomalous objects through the use of multivariate statistics. Global anomaly detectors, such as the autonomous global anomaly detector (AutoGAD), require the user to provide various parameters/thresholds to analyse an image. These user-defined settings can be thought of as control variables and properties of the imagery can be employed as noise variables. The presence of these factors suggests the use of robust parameter design (RPD) to locate the best settings for the algorithm. Mindrup et al. (2012) showed that the standard RPD model might not be sufficient for use with more complex data and extended the model to include noise by noise interactions. This paper extends the model to include control by noise by noise and noise by control by control interactions. These new models are then applied to AutoGAD output and the Lin and Tu MSE method is employed to locate optimal settings.

Keywords: anomaly detection; autonomous global anomaly detector; AutoGAD; hyperspectral imagery; HSI; independent component analysis; ICA; response surface methodology; RSM; robust parameter design; RPD; anomalous objects; multivariate statistics; image analysis.

DOI: 10.1504/IJQET.2013.053258

International Journal of Quality Engineering and Technology, 2013 Vol.3 No.3, pp.204 - 218

Received: 03 May 2012
Accepted: 25 May 2012
Published online: 19 Jul 2014 *

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Title: Further extensions to robust parameter design: three factor interactions with an application to hyperspectral imagery

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