TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

Abstract

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.

References in corpus (6)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Unsupervised Domain Adaptation by Backpropagation
• Deep Domain Confusion: Maximizing for Domain Invariance
• Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data
• Unbalanced minibatch Optimal Transport; applications to Domain Adaptation

Cited by in corpus (5)

• Deep Learning for Satellite Image Time Series Analysis: A Review
• A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
• Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting
• Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
• From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping