1. DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs
- Author
-
Verburg, Corné, Heinlein, Alexander, and Cyr, Eric C.
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Machine Learning ,68T07, 68W10, 68W15, 65N55, 68U10 ,I.2.6 ,I.4.6 - 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.
- Published
- 2024