Multilevel analytical and visual decision framework for imagery conflation and registration

This chapter addresses imagery conflation and registration problems by providing an Analytical and Visual Decision Framework (AVDF). This framework recognizes that pure analytical methods are not sufficient for integrating images. Conflation refers to a process similar but more complex than what is traditionally called registration, in the sense that there is, at least, some conflicting information, which predates it and post conflation evaluation that postdates it. The conflation process studies the cases of two or more data sources where each has inaccuracy and none of them is perfect. The chapter covers complexity space, conflation levels, error structure analysis, and a rules-based conflation scenario. Without AVDF, the mapping between two input data sources is more opportunistic then definitive. A partial differential equation approach is used to illustrate the modeling of disparities between data sources for a given mapping function. A specific case study of AVDF for pixel-level conflation is presented based on Shannon’s concept of mutual entropy.

Key words: Imagery conflation, registration, analytical and visual decision framework, complexity space, conflation level, rule base, entropy, mutual information.