An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture 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. We demonstrate examples for search-and-rescue, wildfire detection, and wildlife observation.

翻译：多输入通道中显著信息的充分融合对于许多航拍成像任务至关重要。多光谱记录能够揭示不同光谱范围内的特征，而合成孔径感知技术可使被遮挡的特征变得可见。我们提出了一种首个混合（基于模型与学习）架构，用于融合传统航拍图像中最显著的特征与来自积分航拍图像（合成孔径感知去除遮挡的结果）的特征。该方法将环境空间参考与通常被茂密植被隐藏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，在视觉和定量方面均优于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构。我们展示了该模型在搜索救援、野火探测和野生动物观测中的应用实例。