Extending robust parameter design to noise by noise interactions with an application to hyperspectral imagery. (1st January 2012) - Mindrup, Frank M.; Bauer, Kenneth W.; Friend, Mark A.
Anomaly detection algorithms for hyperspectral imagery (HSI) are an important first step in the analysis chain. Improvements to anomaly detection algorithm effectiveness directly reduce the amount of data that must go through time intensive processing before final analysis. The effectiveness of most anomaly detection algorithms is a function of user selected algorithm parameters, or controls, and uncontrollable noise factors which introduce additional variance into the detection process. In the case of HSI, these noise factors are embedded in the image under consideration. Robust parameter design (RPD) offers a method to reduce the impact of these noise variables on anomaly detection algorithms by identifying control settings robust to the noise factors found in HSI. This paper extends standard RPD modelling by removing the assumption that squared noise terms and noise by noise interactions are negligible. A mean squared error approach proposed by Lin and Tu is applied which employs expected value and variance models to solve for optimal settings. Results from an experiment using the autonomous global anomaly detection (AutoGAD) algorithm with both the standard RSM model and the proposed model are provided as an illustration. The sum of label accuracy and true positive fraction was considered as the AutoGAD response of interest.