  The segmentation of ultra-high resolution images poses challenges such as
loss of spatial information or computational inefficiency. In this work, a
novel approach that combines encoder-decoder architectures with domain
decomposition strategies to address these challenges is proposed. Specifically,
a domain decomposition-based U-Net (DDU-Net) architecture is introduced, which
partitions input images into non-overlapping patches that can be processed
independently on separate devices. A communication network is added to
facilitate inter-patch information exchange to enhance the understanding of
spatial context. Experimental validation is performed on a synthetic dataset
that is designed to measure the effectiveness of the communication network.
Then, the performance is tested on the DeepGlobe land cover classification
dataset as a real-world benchmark data set. The results demonstrate that the
approach, which includes inter-patch communication for images divided into
$16\times16$ non-overlapping subimages, achieves a $2-3\,\%$ higher
intersection over union (IoU) score compared to the same network without
inter-patch communication. The performance of the network which includes
communication is equivalent to that of a baseline U-Net trained on the full
image, showing that our model provides an effective solution for segmenting
ultra-high-resolution images while preserving spatial context. The code is
available at https://github.com/corne00/HiRes-Seg-CNN.
