  Including information from additional spectral bands (e.g., near-infrared)
can improve deep learning model performance for many vision-oriented tasks.
There are many possible ways to incorporate this additional information into a
deep learning model, but the optimal fusion strategy has not yet been
determined and can vary between applications. At one extreme, known as "early
fusion," additional bands are stacked as extra channels to obtain an input
image with more than three channels. At the other extreme, known as "late
fusion," RGB and non-RGB bands are passed through separate branches of a deep
learning model and merged immediately before a final classification or
segmentation layer. In this work, we characterize the performance of a suite of
multispectral deep learning models with different fusion approaches, quantify
their relative reliance on different input bands and evaluate their robustness
to naturalistic image corruptions affecting one or more input channels.
