  The advent of satellite-borne machine learning hardware accelerators has
enabled the on-board processing of payload data using machine learning
techniques such as convolutional neural networks (CNN). A notable example is
using a CNN to detect the presence of clouds in hyperspectral data captured on
Earth observation (EO) missions, whereby only clear sky data is downlinked to
conserve bandwidth. However, prior to deployment, new missions that employ new
sensors will not have enough representative datasets to train a CNN model,
while a model trained solely on data from previous missions will underperform
when deployed to process the data on the new missions. This underperformance
stems from the domain gap, i.e., differences in the underlying distributions of
the data generated by the different sensors in previous and future missions. In
this paper, we address the domain gap problem in the context of on-board
hyperspectral cloud detection. Our main contributions lie in formulating new
domain adaptation tasks that are motivated by a concrete EO mission, developing
a novel algorithm for bandwidth-efficient supervised domain adaptation, and
demonstrating test-time adaptation algorithms on space deployable neural
network accelerators. Our contributions enable minimal data transmission to be
invoked (e.g., only 1% of the weights in ResNet50) to achieve domain
adaptation, thereby allowing more sophisticated CNN models to be deployed and
updated on satellites without being hampered by domain gap and bandwidth
limitations.
