DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs

• URL: http://arxiv.org/abs/2407.21266v2
• Date: Thu, 1 Aug 2024 01:59:58 GMT
• Title: DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs
• Authors: Corné Verburg, Alexander Heinlein, Eric C. Cyr,
• Abstract summary: A domain decomposition-based U-Net architecture is introduced, which partitions input images into non-overlapping patches. A communication network is added to facilitate inter-patch information exchange to enhance the understanding of spatial context. Results show that the approach achieves a $2-3,%$ higher intersection over union (IoU) score compared to the same network without inter-patch communication.
• Score: 46.873264197900916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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