Quantitative fusion of performance results from actual and simulated image data

Abstract

Simulated imagery is a useful adjunct to actual imagery collected from a sensor platform. Simulation allows control of multiple parameters and combinations of parameters that might otherwise be difficult to capture in an actual measurement, leading to a fuller understanding of processes and phenomenology under consideration. However, the complexity that exists in actual, measured imagery can be difficult to capture in simulation. Such complexity, coupled with the other natural ambiguities of measured data, makes it difficult to compare results achieved from algorithms applied to simulated imagery with algorithmic results achieved with actual data. We demonstrate the use of Sequential Quantitative Performance Assessment (SQPA) as a means of fusing results from simulated and actual imagery.