TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

• URL: http://arxiv.org/abs/2111.02682v1
• Date: Thu, 4 Nov 2021 08:32:59 GMT
• Title: TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
• Authors: Joachim Nyborg, Charlotte Pelletier, S\'ebastien Lef\`evre, Ira Assent
• Abstract summary: TimeMatch is a new unsupervised domain adaptation method for SITS that directly accounts for the temporal shift. TimeMatch outperforms all competing methods by 11% in F1-score across five different adaptation scenarios. We introduce an open-access dataset for cross-region adaptation with SITS from four different regions in Europe.
• Score: 5.027714423258537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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