  Synthetic apertures find applications in many fields, such as radar, radio
telescopes, microscopy, sonar, ultrasound, LiDAR, and optical imaging. They
approximate the signal of a single hypothetical wide aperture sensor with
either an array of static small aperture sensors or a single moving small
aperture sensor. Common sense in synthetic aperture sampling is that a dense
sampling pattern within a wide aperture is required to reconstruct a clear
signal. In this article we show that there exists practical limits to both,
synthetic aperture size and number of samples for the application of occlusion
removal. This leads to an understanding on how to design synthetic aperture
sampling patterns and sensors in a most optimal and practically efficient way.
We apply our findings to airborne optical sectioning which uses camera drones
and synthetic aperture imaging to computationally remove occluding vegetation
or trees for inspecting ground surfaces.
