Multi-spectral image shift-estimation error calculations using simulated phenomenology

Registration of multi-spectral imagery is a critical pre-processing step for applications such as image fusion, but phenomenological differences between spectral bands can lead to significant estimation errors. To develop credible requirements for multi-spectral imaging systems, it is critical to characterize errors, both algorithmic and fundamental, associated with estimating registration parameters; however, attempting to quantify error using archival data sets poses a number of problems. In this paper, we demonstrate the use of commercially available graphics software and available optical property measurements to create fully synthetic, multi-spectral imagery with high-fidelity representations of emissive and reflective phenomenology. We discuss and demonstrate techniques needed to quantify error for both area- and feature-based algorithms. We further show that such synthetic data sets can be used to quantify both the Fisher information and sample errors associated with estimation of the shift between images acquired in different spectral bands and, by extension, estimation of registration model parameters. With the flexibility offered by synthetic data, such characterization can be obtained for robust domains of image brightness, sensor parameters, and differences in image phenomenology.