DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs
<|reference_start|>DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs: 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.<|reference_end|>
@article{verburg2024ddu-net:,
    title={DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image
  Segmentation on Multiple GPUs},
    author={Corn'e Verburg, Alexander Heinlein, Eric C. Cyr},
    journal={arXiv preprint arXiv:2407.21266},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21266},
    primaryClass={cs.CV cs.DC cs.LG}
}