# Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualization

arXiv: https://arxiv.org/abs/2005.04065 · HTML: https://mlsanity.com/paper/2005.04065/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 14 of 14)

- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [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)
- [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)
- [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)

In this article, we describe and validate the first fully automatic parameter optimization for thermal synthetic aperture visualization. It replaces previous manual exploration of the parameter space, which is time consuming and error prone. We prove that the visibility of targets in thermal integral images is proportional to the variance of the targets' image. Since this is invariant to occlusion it represents a suitable objective function for optimization. Our findings have the potential to enable fully autonomous search and recuse operations with camera drones.