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New multi-axis satellites allow camera imaging parameters to be set during each time slot based on competing demand for images, specified as rectangular requested viewing zones over the camera's reachable field of view. The single frame selection (SFS) problem is to find the camera frame parameters that maximize reward during each time window. We formalize the SFS problem based on a new reward metric that takes into account area coverage and image resolution. For a set of n client requests and a satellite with m discrete resolution levels, we give an algorithm that solves the SFS problem in time O(n2m). For satellites with continuously variable resolution (m=∞), we give an algorithm that runs in time O(n3). We have implemented all algorithms and verify performance using random inputs. Note to Practitioners-This paper is motivated by recent innovations in earth imaging by commercial satellites. In contrast to previous methods that required waits of up to 21 days for desired earth- satellite alignment, new satellites have onboard pan-tilt-zoom cameras that can be remotely directed to provide near real-time response to requests for images of specific areas on the earth's surface. We consider the problem of resolving competing requests for images: Given client demand as a set of rectangles on the earth surface, compute camera settings that optimize the tradeoff between pan, tilt, and zoom parameters to maximize camera revenue during each time slot. We define a new quality metric and algorithms for solving the problem for the cases of discrete and continuous zoom values. These results are a step toward multiple frame selection which will be addressed in future research. The metric and algorithms presented in this paper may also be applied to collaborative teleoperation of ground-based robot cameras for inspection and videoconferencing and for scheduling astronomic telescopes.