Exploitation of massive numbers of simple events
Ray Rimey, Dan Keefe · 2008
Emerging image-based sensor systems can observe a relatively large area (e.g., the size of an urban neighborhood) for long time intervals either continually or with high revisit rates. This type of sensor data makes new types of exploitation possible, but only with the assistance of automated exploitation aids because of the massive volume of data that must be studied as a whole. Automated methods to extract the simplest events from image sequences are often fairly robust (e.g., change events derived from EO or SAR image sequences or from video-derived tracks). Massive numbers of such events can contain information with high intelligence value. This paper examines this general-purpose problem: How massive numbers of the simplest sensor-derived events can be exploited. We summarize the basic functionality an intelligence analyst needs for studying this type of event data, in short: to understand the spatial structure, temporal structure and event-pair structure within an area of regard. Then we present a number of algorithms for automated exploitation of such data, and some visualization tools to help analysts study such data. Experimental results using all those technologies are also presented.