Virtual Experts for Imagery Registration and Conflation

The unique human expertise in imagery analysis should be preserved and shared with other imagery analysts to improve image analysis and decision-making. Such knowledge can serve as a corporate memory and be a base for an imagery virtual expert. The core problem in reaching this goal is constructing a methodology and tools that can assist in building the knowledge base of imagery analysis. This paper provides a framework for an imagery virtual expert system that supports imagery registration and conflation tasks. The approach involves tree strategies: (1) recording expertise on-the-fly and (2) ex-extracting information from the expert in an optimized way using the theory of monotone Boolean functions and (3) use of iconized ontologies to build a conflation method. The paper presents an ontological iconic registration/conflation method based on this methodology that is implemented as an ArcGIS Plug-in. To be able to do this we build an OWL ontology for the Feature Attribute Coding Catal...

• Type: Conference
• Year: 2003
• Where: CISST
• Authors: Boris Kovalerchuk, Artemus Harper, Michael Kovalerchuk
• Keywords: CISST 2003, Hardware, Imagery Analysis, Imagery Analysts, Imagery Virtual Expert