  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.
