A novel hybrid (model- and learning-based) architecture is presented for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment's spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method out-beats state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and combinations of spectral channels, and is reconfigurable for addressing different use cases.

翻译：本文提出了一种新颖的混合架构（融合模型驱动与数据驱动方法），用于将传统航拍图像中的显著特征与积分航拍图像的特征相融合，其中积分图像通过合成孔径感知技术去除遮挡效应。该架构将环境空间参考信息与通常被茂密植被遮挡的无遮挡目标特征相结合。我们的方法在互信息、视觉信息保真度和峰值信噪比等通用指标上，无论在视觉上还是定量上均优于当前最先进的双通道和多通道融合方法。所提出的模型无需手动调整参数，可扩展至任意数量及组合的光谱通道，并可针对不同应用场景进行重新配置。