  Modern deep neural networks (DNNs) achieve highly accurate results for many
recognition tasks on overhead (e.g., satellite) imagery. One challenge however
is visual domain shifts (i.e., statistical changes), which can cause the
accuracy of DNNs to degrade substantially and unpredictably when tested on new
sets of imagery. In this work we model domain shifts caused by variations in
imaging hardware, lighting, and other conditions as non-linear pixel-wise
transformations; and we show that modern DNNs can become largely invariant to
these types of transformations, if provided with appropriate training data
augmentation. In general, however, we do not know the transformation between
two sets of imagery. To overcome this problem, we propose a simple real-time
unsupervised training augmentation technique, termed randomized histogram
matching (RHM). We conduct experiments with two large public benchmark datasets
for building segmentation and find that RHM consistently yields comparable
performance to recent state-of-the-art unsupervised domain adaptation
approaches despite being simpler and faster. RHM also offers substantially
better performance than other comparably simple approaches that are widely-used
in overhead imagery.
