# Inverse Airborne Optical Sectioning

arXiv: https://arxiv.org/abs/2207.13344 · HTML: https://mlsanity.com/paper/2207.13344/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [Search and Rescue with Airborne Optical Sectioning](https://mlsanity.com/paper/2009.08835/) (2020)
- [A Statistical View on Synthetic Aperture Imaging for Occlusion Removal](https://mlsanity.com/paper/1906.06600/) (2019)
- [Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualization](https://mlsanity.com/paper/2005.04065/) (2020)
- [Pose Error Reduction for Focus Enhancement in Thermal Synthetic Aperture Visualization](https://mlsanity.com/paper/2012.08606/) (2020)
- [Combined Person Classification with Airborne Optical Sectioning](https://mlsanity.com/paper/2106.10077/) (2021)
- [Through-Foliage Tracking with Airborne Optical Sectioning](https://mlsanity.com/paper/2111.06959/) (2021)
- [On the Role of Field of View for Occlusion Removal with Airborne Optical Sectioning](https://mlsanity.com/paper/2204.13371/) (2022)
- [Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images](https://mlsanity.com/paper/2101.12535/) (2021)

## Built on by (most cited first, 9 of 9)

- [Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing](https://mlsanity.com/paper/2211.04293/) (2022)
- [Fusion of Single and Integral Multispectral Aerial Images](https://mlsanity.com/paper/2311.17515/) (2023)
- [Stereoscopic Depth Perception Through Foliage](https://mlsanity.com/paper/2310.16120/) (2023)
- [An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation](https://mlsanity.com/paper/2407.10754/) (2024)
- [DeepForest: Sensing Into Self-Occluding Volumes of Vegetation With Aerial Imaging](https://mlsanity.com/paper/2502.02171/) (2025)
- [Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection](https://mlsanity.com/paper/2511.12572/) (2025)
- [Synthetic Aperture Sensing for Occlusion Removal with Drone Swarms](https://mlsanity.com/paper/2212.14692/) (2022)
- [Synthetic Aperture Anomaly Imaging](https://mlsanity.com/paper/2304.13590/) (2023)
- [Vision-Reasoning-Guided Occlusion Removal from Light Fields](https://mlsanity.com/paper/2606.19985/) (2026)

## Abstract (arXiv)

We present Inverse Airborne Optical Sectioning (IAOS) an optical analogy to Inverse Synthetic Aperture Radar (ISAR). Moving targets, such as walking people, that are heavily occluded by vegetation can be made visible and tracked with a stationary optical sensor (e.g., a hovering camera drone above forest). We introduce the principles of IAOS (i.e., inverse synthetic aperture imaging), explain how the signal of occluders can be further suppressed by filtering the Radon transform of the image integral, and present how targets motion parameters can be estimated manually and automatically. Finally, we show that while tracking occluded targets in conventional aerial images is infeasible, it becomes efficiently possible in integral images that result from IAOS.