Spectral imaging system performance forecasting

Abstract

The quantitative forecasting of spectral imaging system performance is an important capability at every stage of system development including system requirement definition, system design, and even sensor operation. However, due to the complexity of the end-to-end remote sensing system involved, the analyses are often performed piecemeal by various groups, and then merged together. The ability to understand system sensitivities also supports the best use of an operational system and is thus desirable. It was with this perspective and goal to better perform end-to-end remote sensing system analyses that work was undertaken in the late 1980s to develop models that can be efficiently used as part of the system design and operation. Both simulation and analytical models were developed. The simulation approach has the advantage of creating an actual image, which can include non-linear effects or specified instrument artifacts, while the analytical approach has the benefit of being much simpler computationally and amenable to large numbers of comprehensive trade studies. In the mid 1990s, the analytical approach was extended to the case of unresolved object detection. By taking advantage of the spectral information, objects and materials that are not spatially resolved in the imagery can still be detected and identified. Subsequently, this model, which was developed for the reflective solar part of the optical spectrum, was extended to the thermal infrared. Here, surfaces are characterized not only by, their spectral emissivity means and covariances, but also their physical temperature mean and standard deviation. The model has also been extended to explore linear unmixing applications through the implementation of multiple classes in the target class. This has allowed the exploration of the role of class variability in unmixing abundance estimation. This paper provides an overview of this model development activity as well as show examples of how it can be used in the various applications. Examples include the impact of system parameters sub-pixel object detection and abundance estimation applications. Key capabilities as well as limitations of this analytical modeling approach are identified. System understanding developed through the use of the model is highlighted and the future enhancements are discussed.