# A Statistical View on Synthetic Aperture Imaging for Occlusion Removal

## TL;DR

This paper explores the limits of synthetic aperture imaging for occlusion removal, revealing practical constraints on aperture size and sampling density, and applying these insights to drone-based optical imaging.

## Contribution

It provides a statistical analysis of sampling limits in synthetic aperture imaging, guiding optimal sensor and pattern design for occlusion removal applications.

## Key findings

- Identifies practical limits to aperture size and sampling density.
- Offers guidelines for designing efficient synthetic aperture sampling patterns.
- Demonstrates application in drone-based optical sectioning for ground inspection.

## Abstract

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.