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The sensor to shooter timeline is affected by two main variables: satellitepositioning and asset positioning. Speeding up satellite positioning by addingmore sensors or by decreasing processing time is important only if there is aprepared shooter, otherwise the main source of time is getting the shooter intoposition. However, the intelligence community should work towards theexploitation of sensors to the highest speed and effectiveness possible.Achieving a high effectiveness while keeping speed high is a tradeoff that mustbe considered in the sensor to shooter timeline. In this paper we investigatetwo main ideas, increasing the effectiveness of satellite imagery through imagemanipulation and how on-board image manipulation would affect the sensor toshooter timeline. We cover these ideas in four scenarios: Discrete EventSimulation of onboard processing versus ground station processing, quality ofinformation with cloud cover removal, information improvement with superresolution, and data reduction with image to caption. This paper will show howimage manipulation techniques such as Super Resolution, Cloud Removal, andImage to Caption will improve the quality of delivered information in additionto showing how those processes effect the sensor to shooter timeline.

Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline