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Near real time satellite imaging provides timely images of the earth for weather prediction, disaster response, search and rescue, surveillance, and defense applications. As the satellite passes over the earth, camera imaging parameters are changed during each time window based on demand for images, specified as user requested zones in the reachable field of view during that time window. The satellite frame selection (SFS) problem is to find the camera frame parameters that maximize reward during each time window. To automate satellite management, we formalize the SFS problem based on a new reward metric that incorporates both image resolution and coverage. For a set of n client requests we give a series of algorithms, the fastest computes optimal results in O(n3) for satellites with continuously variable resolution. We have implemented the algorithms and compare computation speed for all algorithms.