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

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

Blog Article

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 animed aniflex complete curating suitable pretext tasks.While bimodal self-supervised approaches combining data from Sentinel-2 socialstudiesscholar.com 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.