Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring
<|reference_start|>Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring: 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.<|reference_end|>
@article{zhao2024prediction,
    title={Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for
  continuous earth surface monitoring},
    author={Weiying Zhao, Natalia Efremova},
    journal={arXiv preprint arXiv:2407.00834},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00834},
    primaryClass={cs.IR}
}