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arXiv · 2407.00834

Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring

Weiying Zhao·Natalia Efremova
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Abstract

Continuous monitoring of crops and forecasting crop conditions through time series analysis is crucial for effective agricultural management. This study proposes a framework based on an attention Bidirectional Long Short-Term Memory (BiLSTM) network for predicting multiband images. Our model can forecast target images on user-defined dates, including future dates and periods characterized by persistent cloud cover. By focusing on short sequences within a sequence-to-one forecasting framework, the model leverages advanced attention mechanisms to enhance prediction accuracy. Our experimental results demonstrate the model's superior performance in predicting NDVI, multiple vegetation indices, and all Sentinel-2 bands, highlighting its potential for improving remote sensing data continuity and reliability.

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Weiying Zhao, Natalia Efremova. 2024-06-30. Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring. https://arxiv.org/abs/2407.00834

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