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.

翻译：先前研究表明，在存在树叶遮挡的情况下，相较于直接应用于传统航拍图像，将异常检测应用于合成孔径成像生成的积分图像具有显著更优的性能。本文提出并证明：对检测到的异常进行积分处理比在积分图像中检测异常更为有效。这一方法能增强遮挡消除能力、抑制离群值，并提高在视觉与计算层面检测原本被遮挡目标的概率。该假设已通过仿真实验与外场实验双重验证。我们还提出了一套实时应用系统，使我们的研究成果能够实际服务于执法机构及其他使用商用无人机平台的用户。该系统专为解决因植被遮挡导致的强遮挡场景而设计，例如搜索救援、野生动物观测、早期野火探测及监控等应用场景。