The recent developments of deep learning models that capture the complex temporal patterns of crop phenology have greatly advanced crop classification of Satellite Image Time Series (SITS). However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions. To address this unsupervised cross-region adaptation setting, existing methods learn domain-invariant features without any target supervision, but not the temporal shift itself. As a consequence, these techniques provide only limited benefits for SITS. In this paper, we propose TimeMatch, a new unsupervised domain adaptation method for SITS that directly accounts for the temporal shift. TimeMatch consists of two components: 1) temporal shift estimation, which estimates the temporal shift of the unlabeled target region with a source-trained model, and 2) TimeMatch learning, which combines temporal shift estimation with semi-supervised learning to adapt a classifier to an unlabeled target region. We also introduce an open-access dataset for cross-region adaptation with SITS from four different regions in Europe. On this dataset, we demonstrate that TimeMatch outperforms all competing methods by 11% in F1-score across five different adaptation scenarios, setting a new state-of-the-art for cross-region adaptation.

提出了一种面向卫星图像时间序列的作物分类跨区域适应技术 TimeMatch，考虑了时间漂移的影响，通过估算目标区域与源区域之间的时间漂移并重新训练模型来提高适应性，在跨欧洲四个区域的数据集中，TimeMatch 的实验平均 F1 得分优于所有其他方法 11%，在跨区域适应中创下了最新的技术水平。

TimeMatch：通过时间偏移估计进行无监督的跨区域适应