A Synergistic Exploitation Concept for Wide Area Search

Key to effective image exploitation is letting man and machine do what they each do best. Automated target recognition (ATR) systems rely on model-based approaches that operate in a highly structured predict-extract-match-search (PEMS) loop. Fundamental to wide area search (WAS) is the problem of detecting a large number of potential objects that are so diverse in nature that they cannot be enumerated let alone modeled. In addition there is the problem of unexpected objects (i.e., those that cannot be modeled a priori). A new approach to search based on a detect-organize-compile (DOC) paradigm is proposed and applied to wide area change detection (WACD). It combines an automated image screening algorithm for detecting manmade changes with an interactive web-based tool for ranking, reviewing, and compiling changes of interest. Performance of this approach against exhaustive manual search of the image shows about a 10X increase in throughput (changes detected per unit time) at a fixed level of performance.