We present a novel hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images and integral aerial images that result from synthetic aperture sensing for removing occlusion caused by dense vegetation. It combines the environment's spatial references with features of unoccluded targets. Our method out-beats the state-of-the-art, does not require manually tuned parameters, can be extended to an arbitrary number and combinations of spectral channels, and is reconfigurable to address different use-cases.

翻译：我们提出了一种新颖的混合架构（基于模型与基于学习相结合），用于融合传统航空图像与积分式航空图像中最显著的特征——后者源自合成孔径感知技术，旨在消除茂密植被造成的遮挡。该方法将环境的空间参考信息与无遮挡目标的特征相结合。我们的方法超越现有技术水平，无需手动调整参数，可扩展至任意数量及组合的光谱通道，并能根据不同应用场景进行重构配置。