Abstract: Both humans and computational methods struggle to discriminate the depth of objects hidden under foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing with human’s ability to fuse stereoscopic images. For object identification tasks, as required in search and rescue, wildlife observation, surveillance, or early wildfire detection, depth provides an additional hint to differentiate between true and false findings, such as people, animals, or vehicles vs. sun-heated patches on the ground surfaces or the tree crowns, or ground fires vs. tree trunks. We used video captured by a drone above dense forest to test user’s ability to discriminate depth. We found that discriminating the depth of objects is infeasible when inspecting monoscopic video and relying on motion parallax. This was also impossible for stereoscopic video because of the occlusions from the foliage. However, when the occlusions were reduced with synthetic aperture sensing and disparity-scaled stereoscopic video was presented, human observers were successful in the depth discrimination. At the same time, computational (stereoscopic matching) methods were unsuccessful. This shows the potential of systems which use the synergy of computational methods and human vision to perform tasks that are infeasible for either of them alone.