  In the application of machine learning to remote sensing, labeled data is
often scarce or expensive, which impedes the training of powerful models like
deep convolutional neural networks. Although unlabeled data is abundant, recent
self-supervised learning approaches are ill-suited to the remote sensing
domain. In addition, most remote sensing applications currently use only a
small subset of the multi-sensor, multi-channel information available,
motivating the need for fused multi-sensor representations. We propose a new
self-supervised training objective, Contrastive Sensor Fusion, which exploits
coterminous data from multiple sources to learn useful representations of every
possible combination of those sources. This method uses information common
across multiple sensors and bands by training a single model to produce a
representation that remains similar when any subset of its input channels is
used. Using a dataset of 47 million unlabeled coterminous image triplets, we
train an encoder to produce semantically meaningful representations from any
possible combination of channels from the input sensors. These representations
outperform fully supervised ImageNet weights on a remote sensing classification
task and improve as more sensors are fused. Our code is available at
https://storage.cloud.google.com/public-published-datasets/csf_code.zip.
