In the intelligence community, the volume of imagery data threatens to overwhelm the traditional process of information extraction. Satellite systems are capable of producing large quantities of imagery data every day. Traditionally, intelligence analysts have the arduous task of manually reviewing satellite imagery data and generating information products. In a time of increasing imagery data, this manual approach is not consistent with the goal of a timely and highly responsive system.
These realities are key factors in Booz Allen Hamilton's transformational approach to information extraction. This approach employs information services and value added processes (VAP) to reduce the amount of data being manually reviewed. Booz Allen has utilized a specialization/generalization hierarchy to aggregate hundreds of thousands of imagery intelligence needs into sixteen information services. Information Services are automated by employing value added processes, which extract the information from the imagery data and generate information products. While the intelligence needs and information services remain relatively static in time, the VAP's have the ability to evolve rapidly with advancing technologies.
The Booz Allen Transformational Information Extraction Model validates this automated approach by simulating realistic system parameters. The functional flow model includes image formation, three information services, six VAP's, and reduced manual intervention. Adjustable model variables for VAP time, VAP confidence, number of intelligence analyst, and time for analyst review provide a flexible framework for modeling different system cases. End-to-End system metrics such as intelligence need satisfaction, end-to-end timeliness, and sensitivity to number of analyst and VAP variables quantify the system performance.