Synthetic Aperture Anomaly Imaging
This addresses the problem of strong occlusion in applications like search and rescue and surveillance for blue-light organizations and drone users, though it appears incremental.
The paper tackled the problem of detecting anomalies under foliage occlusion by proposing that integrating detected anomalies is more effective than detecting anomalies in integral images, resulting in enhanced occlusion removal and higher detection chances, validated through simulations and field experiments.
Previous research has shown that in the presence of foliage occlusion, anomaly detection performs significantly better in integral images resulting from synthetic aperture imaging compared to applying it to conventional aerial images. In this article, we hypothesize and demonstrate that integrating detected anomalies is even more effective than detecting anomalies in integrals. This results in enhanced occlusion removal, outlier suppression, and higher chances of visually as well as computationally detecting targets that are otherwise occluded. Our hypothesis was validated through both: simulations and field experiments. We also present a real-time application that makes our findings practically available for blue-light organizations and others using commercial drone platforms. It is designed to address use-cases that suffer from strong occlusion caused by vegetation, such as search and rescue, wildlife observation, early wildfire detection, and sur-veillance.