  Multi-spectral imagery is invaluable for remote sensing due to different
spectral signatures exhibited by materials that often appear identical in
greyscale and RGB imagery. Paired with modern deep learning methods, this
modality has great potential utility in a variety of remote sensing
applications, such as humanitarian assistance and disaster recovery efforts.
State-of-the-art deep learning methods have greatly benefited from large-scale
annotations like in ImageNet, but existing MSI image datasets lack annotations
at a similar scale. As an alternative to transfer learning on such data with
few annotations, we apply complex-valued co-domain symmetric models to classify
real-valued MSI images. Our experiments on 8-band xView data show that our
ultra-lean model trained on xView from scratch without data augmentations can
outperform ResNet with data augmentation and modified transfer learning on
xView. Our work is the first to demonstrate the value of complex-valued deep
learning on real-valued MSI data.
