# Combined Person Classification with Airborne Optical Sectioning

arXiv: https://arxiv.org/abs/2106.10077 · HTML: https://mlsanity.com/paper/2106.10077/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [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)
- [Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture Recognition](https://mlsanity.com/paper/1804.07187/) (2018)
- [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)

## Built on by (most cited first, 13 of 13)

- [Through-Foliage Tracking with Airborne Optical Sectioning](https://mlsanity.com/paper/2111.06959/) (2021)
- [Inverse Airborne Optical Sectioning](https://mlsanity.com/paper/2207.13344/) (2022)
- [Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing](https://mlsanity.com/paper/2211.04293/) (2022)
- [On the Role of Field of View for Occlusion Removal with Airborne Optical Sectioning](https://mlsanity.com/paper/2204.13371/) (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)
- [How Robot Dogs See the Unseeable: Improving Visual Interpretability via Peering for Exploratory Robots](https://mlsanity.com/paper/2511.16262/) (2025)
- [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)

Fully autonomous drones have been demonstrated to find lost or injured persons under strongly occluding forest canopy. Airborne Optical Sectioning (AOS), a novel synthetic aperture imaging technique, together with deep-learning-based classification enables high detection rates under realistic search-and-rescue conditions. We demonstrate that false detections can be significantly suppressed and true detections boosted by combining classifications from multiple AOS rather than single integral images. This improves classification rates especially in the presence of occlusion. To make this possible, we modified the AOS imaging process to support large overlaps between subsequent integrals, enabling real-time and on-board scanning and processing of groundspeeds up to 10 m/s.