  Modern deep neural networks (DNNs) are highly accurate on many recognition
tasks for overhead (e.g., satellite) imagery. However, visual domain shifts
(e.g., statistical changes due to geography, sensor, or atmospheric conditions)
remain a challenge, causing the accuracy of DNNs to degrade substantially and
unpredictably when testing 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 perform a
systematic study indicating that modern DNNs can become largely robust 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, we propose a fast real-time unsupervised
training augmentation technique, termed randomized histogram matching (RHM). We
conduct experiments with two large benchmark datasets for building segmentation
and find that despite its simplicity, RHM consistently yields similar or
superior performance compared to state-of-the-art unsupervised domain
adaptation approaches, while being significantly simpler and more
computationally efficient. RHM also offers substantially better performance
than other comparably simple approaches that are widely used for overhead
imagery.
