The domain adaptation of satellite images has recently gained increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions since, nowadays, multiple sources and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multisource, multitarget, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches.

中文翻译：

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DAugNet：卫星图像语义分割的无监督、多源、多目标和终身域适应

卫星图像的域适应最近受到越来越多的关注，以克服机器学习模型在分割大规模卫星图像时的泛化能力有限。大多数现有方法都寻求将模型从一个域调整到另一个域。然而，这种单源和单目标设置阻止了这些方法成为可扩展的解决方案，因为如今通常可以使用具有不同数据分布的多个源和目标域。此外，卫星图像的不断扩散需要分类器来适应不断增加的数据。我们提出了一种新方法，创造了 DAugNet，用于卫星图像的无监督、多源、多目标和终身域适应。它由一个分类器和一个数据增强器组成。数据增强器，这是一个浅层网络，即使随着时间的推移添加新数据，也能够以无监督的方式在多个卫星图像之间执行样式转换。在每次训练迭代中，它为分类器提供多样化的数据，这使得分类器对域间大数据分布差异具有鲁棒性。我们广泛的实验证明，与现有方法相比，DAugNet 可以更好地泛化到新的地理位置。