TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

Joachim Nyborg (1) , Charlotte Pelletier (2) , Sébastien Lefèvre (2) , Ira Assent (1)

1 Aarhus University [Aarhus]
2 OBELIX - Observation de l’environnement par imagerie complexe

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.

Mots clés: Satellite Image Time Series; Temporal Shift; Crop Classification; Domain Adaptation; Deep Learning

Domaines: Informatique [cs]

Joachim Nyborg, Charlotte Pelletier, Sébastien Lefèvre, Ira Assent. TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 2022. ⟨hal-03515501⟩