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.

翻译：超高分辨率图像的分割面临着空间信息丢失或计算效率低下等挑战。本文提出了一种新颖的方法，将编码器-解码器架构与区域分解策略相结合以应对这些挑战。具体而言，我们引入了一种基于区域分解的U-Net（DDU-Net）架构，该架构将输入图像划分为不重叠的图像块，这些图像块可以在不同设备上独立处理。我们添加了一个通信网络以促进图像块间的信息交换，从而增强对空间上下文的理解。我们在一个旨在衡量通信网络有效性的合成数据集上进行了实验验证。随后，在作为真实世界基准数据集的DeepGlobe土地覆盖分类数据集上测试了其性能。结果表明，对于划分为$16\times16$个不重叠子图像的图像，采用图像块间通信的方法比没有图像块间通信的相同网络获得了高出$2-3\,\%$的交并比（IoU）分数。包含通信的网络性能与在全图像上训练的基线U-Net相当，这表明我们的模型为分割超高分辨率图像同时保持空间上下文提供了一种有效的解决方案。代码可在https://github.com/corne00/HiRes-Seg-CNN获取。