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

摘要

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.