Image-Derived Prediction of Spectral Image Utility for Target Detection Applications

Abstract

The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. Building on previous research that defines the assessment of the utility of a spectral image based on the detectability of subpixel targets, this paper examines the prediction of spectral image utility. It first reviews existing methods for predicting spectral image utility and then proposes a new approach in predicting spectral image utility for target detection applications that derive statistical parameters directly from the spectral image. This so-called image-derived approach predicts the likelihood of finding synthetically implanted subpixel targets. Using three airborne hyperspectral images, we benchmark prediction performance by comparing the predicted and assessed utilities, quantifying the accuracy of the prediction, and discussing the time savings associated with prediction. Initial results show utility calculation time savings of up to 40% for a single image, with the potential for greater savings if multiple target types are considered. This method of predicting spectral image utility offers a simple and efficient way of predicting image utility, which can be directly compared with our utility assessments.