Synthetic Aperture Anomaly Imaging for Through-Foliage Target Detection

Abstract

The presence of foliage is a serious problem for target detection with drones in application fields such as search and rescue, surveillance, early wildfire detection, or wildlife observation. Visual as well as automatic computational methods, such as classification and anomaly detection, fail in the presence of strong occlusion. Previous research has shown that both benefit from integrating multi-perspective images recorded over a wide synthetic aperture to suppress occlusion. In particular, commonly applied anomaly detection methods can be improved by the more uniform background statistics of integral images. In this article, we demonstrate that integrating the results of anomaly detection applied to single aerial images instead of applying anomaly detection to integral images is significantly more effective and increases target visibility as well as precision by an additional 20% on average in our experiments. This results in enhanced occlusion removal and outlier suppression, and consequently, in higher chances of detecting targets that remain otherwise occluded. We present results from simulations and field experiments, as well as a real-time application that makes our findings available to blue-light organizations and others using commercial drone platforms. Furthermore, we outline that our method is applicable for 2D images as well as for 3D volumes.