  The analysis of satellite imagery will prove a crucial tool in the pursuit of
sustainable development. While Convolutional Neural Networks (CNNs) have made
large gains in natural image analysis, their application to multi-spectral
satellite images (wherein input images have a large number of channels) remains
relatively unexplored. In this paper, we compare different methods of
leveraging multi-band information with CNNs, demonstrating the performance of
all compared methods on the task of semantic segmentation of agricultural
vegetation (vineyards). We show that standard industry practice of using bands
selected by a domain expert leads to a significantly worse test accuracy than
the other methods compared. Specifically, we compare: using bands specified by
an expert; using all available bands; learning attention maps over the input
bands; and leveraging Bayesian optimisation to dictate band choice. We show
that simply using all available band information already increases test time
performance, and show that the Bayesian optimisation, first applied to band
selection in this work, can be used to further boost accuracy.
