  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 outperforms
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 arbitrary combinations of spectral channels, and is reconfigurable for
addressing different use cases. We demonstrate examples for search and rescue,
wildfire detection, and wildlife observation.
