Multivariate methods to explore system sensitivities for hyperspectral subpixel target detection

To explore system sensitivities in hyperspectral subpixel target detection, multivariate methods are applied to output detection metrics generated from a statistical target detection model. The Forecasting and Analysis of Spectroradiometric System Performance (FASSP) statistical model generates probabilities of detection (PD) and false alarm (PFA) using spectral libraries of target and background materials. This allows for the computation of the area under the receiver operating characteristic curve (AUC). To explore sensitivities within elements (e.g. scene, atmosphere, sensor) of the remote sensing system, ensembles of model-based scenarios are generated using combinations of the aerosol visibility, solar angle, and sensor viewing angle. Output detection metrics (PD, AUC) from these scenarios were cached into a high-dimensional tensor, before utilizing multivariate methods (e.g. interpolation and regression) to explore sensitivities and correlations between system variables and detection. Inferences on limitations of detection within the system are drawn from multivariate contour regions which characterize joint parametric parameters required to exceed a desired threshold of detection. The outlined methods aim to provide an initial framework to investigate both specific and generalizable limitations of detection across various scenes (e.g. rural, urban, maritime, and desert), environmental conditions (e.g. solar angle, haze, clouds), sensor characteristics (e.g. noise, viewing angle) and processing configurations (e.g. feature selection, detector algorithm).