The recent developments of deep learning models that capture complex temporal patterns of crop phenology have greatly advanced crop classification from 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. Although various unsupervised domain adaptation techniques have been proposed in recent years, no method explicitly learns the temporal shift of SITS and thus provides only limited benefits for crop classification. To address this, we propose TimeMatch, which explicitly accounts for the temporal shift for improved SITS-based domain adaptation. In TimeMatch, we first estimate the temporal shift from the target to the source region using the predictions of a source-trained model. Then, we re-train the model for the target region by an iterative algorithm where the estimated shift is used to generate accurate target pseudo-labels. Additionally, we introduce an open-access dataset for cross-region adaptation from SITS in four different regions in Europe. On our dataset, we demonstrate that TimeMatch outperforms all competing methods by 11% in average F1-score across five different adaptation scenarios, setting a new state-of-the-art in cross-region adaptation.
翻译:最近开发的深学习模型反映了作物物理学的复杂时间模式,从卫星图象时间序列(SITS)到作物分类的先进程度大大提高了。然而,当应用到与培训区域空间不同的目标区域时,由于不同区域之间作物物理学的时间变化,这些模型在没有任何目标标签的情况下表现不佳。虽然近年来提出了各种未受监督的领域适应技术,但没有方法明确了解SITS的时间变化,因此只能为作物分类提供有限的惠益。为此,我们提议了TimeMatch,其中明确说明了改进基于SITS的域适应的时间变化。在TimeMatch中,我们首先利用对源培训模型的预测来估计目标区域向源区域的时间变化。然后,我们用迭代算法为目标区域重新配置模型,其中估计的转移用于生成准确的目标伪标签。此外,我们从欧洲四个不同区域SITSITS引入了一个开放的跨区域适应数据集。关于我们的数据集,我们证明TimeMatch在平均F1核心适应情景中以11%的相互竞争的方法超越了五个不同的区域。