A Statistical View on Synthetic Aperture Imaging for Occlusion Removal
This work addresses the challenge of efficiently designing sensors and sampling patterns for synthetic aperture imaging in occlusion removal, with incremental improvements for applications like drone-based ground inspection.
The paper tackles the problem of designing synthetic aperture sampling patterns for occlusion removal, showing that there are practical limits to aperture size and sample number, and applies this to airborne optical sectioning for inspecting ground surfaces by removing occluding vegetation.
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.