Fusion of Single and Integral Multispectral Aerial Images
This addresses the challenge of detecting hidden objects in aerial imaging for applications such as search and rescue, wildfire detection, and wildlife observation, representing an incremental advancement in fusion techniques.
The paper tackles the problem of fusing conventional aerial images with integral images from synthetic aperture sensing to reveal occluded targets, achieving superior performance over state-of-the-art methods in metrics like mutual information and peak signal-to-noise ratio.
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.