29 March 1989 A Knowledge-Based Imagery Exploitation System
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Automation of major portions of the imagery exploitation process is becoming a necessity for meeting current and future imagery exploitation needs. In this paper we describe a prototype Automated Exploitation System (AES) which addresses requirements for monitoring objects of interest and situation assessment in large geographic areas. The purpose of AES is to aid the image analyst in performing routine, commonplace tasks more effectively. AES consists of four main subsystems: Cue Extractor (CE), Knowledge-Based Exploitation (KBE), Interactive Work-Station (IWS), and a database subsystem. The CE processes raw image data, and identifies objects and target cues based on pixel- and object-model data. Cues and image registration coefficients are passed to KBE for screening and verification, situation assessment and planning. KBE combines the cues with ground-truth and doctrinal knowledge in screening the cues to determine their importance. KBE generates reports on image analysis which passes on to the IWS from which an image analyst can monitor, observe, and evaluate system functionality as well as respond to critical items identified by KBE. The database subsystem stores and shares reference imagery, collateral information and digital terrain data to support both automated and interactive processing. This partitioning of functions to subsystems facilitates hierarchical application of knowledge in image interpretation. The AES current prototype helps in identification, capture, representation, and refinement of knowledge. The KBE subsystem, which is the primary focus of the present paper, runs on a Symbolics 3675 computer and its software is written in the ART expert system and LISP language.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chuck Smyrniotis, Chuck Smyrniotis, Paul Payton, Paul Payton, Eamon Barrett, Eamon Barrett, } "A Knowledge-Based Imagery Exploitation System", Proc. SPIE 1076, Image Understanding and the Man-Machine Interface II, (29 March 1989); doi: 10.1117/12.952683; https://doi.org/10.1117/12.952683


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