BERT Bi-modal self-supervised learning for crop classification using Sentinel-2 and Planetscope

Crop identification and monitoring of crop dynamics are essential for agricultural planning, environmental monitoring, and ensuring food security. Recent advancements in remote sensing technology and state-of-the-art machine learning have enabled large-scale automated crop classification. However, these methods rely on labeled training data, which requires skilled human annotators or extensive field campaigns, making the process expensive and time-consuming. Self-supervised learning techniques have demonstrated promising results in leveraging large unlabeled datasets across domains. Yet, self-supervised representation learning for crop classification from remote sensing time series remains under-explored due to challenges in curating suitable pretext tasks. While bimodal self-supervised approaches combining data from Sentinel-2 and Planetscope sensors have facilitated pre-training, existing methods primarily exploit the distinct spectral properties of these complementary data sources. In this work, we propose novel self-supervised pre-training strategies inspired from BERT that leverage both the spectral and temporal resolution of Sentinel-2 and Planetscope imagery. We carry out extensive experiments comparing our approach to existing baseline setups across nine test cases, in which our method outperforms the baselines in eight instances. This pre-training thus offers an effective representation of crops for tasks such as crop classification.