The quantity and variety of sensors, available for reconnaissance and surveillance is larger than ever before. However,
increasing the number of sensors results in escalating costs and efforts to analyze the sensor data. Hence it is obvious that
with an increasing number of available sensors the task to determine where reconnaissance requirement is highest and
what sensor combination to use in order to close the information gap becomes more and more complex. Therefore semiautomated
systems are needed to assist the commander of reconnaissance sources. Nevertheless the final decision about
sensor deployment is often made by humans. Thus a module is needed to display the current situation to the decision
maker in a way that allows him to understand the situation at first glance. Using knowledge about what men are better at,
and what machines are better at, we developed on the one hand a mathematical model that allows us determine decision
relevant measures in reconnaissance tasks. On the other hand a visualization concept for reconnaissance requirement,
which visualizes in an intuitive way where reconnaissance requirement is highest, has been developed.
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