Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5793-2022
https://doi.org/10.5194/amt-15-5793-2022
Research article
 | 
14 Oct 2022
Research article |  | 14 Oct 2022

Neural network processing of holographic images

John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne

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Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine learning for precipitation nowcasting from radar images, arXiv [preprint], https://doi.org/10.48550/arXiv.1912.12132, 11 December 2019. a
Berman, M., Triki, A. R., and Blaschko, M. B.: The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 18–22 June 2018, Salt Lake City, Utah, USA, 4413–4421, https://doi.org/10.1109/CVPR.2018.00464, 2018. a
Bernauer, F., Hürkamp, K., Rühm, W., and Tschiersch, J.: Snow event classification with a 2D video disdrometer – A decision tree approach, Atmos. Res., 172, 186–195, 2016. a
Chaurasia, A. and Culurciello, E.: Linknet: Exploiting encoder representations for efficient semantic segmentation, Proceedings of the IEEE Visual Communications and Image Processing (VCIP), 10–13 December 2017, St. Petersburg, FL, USA, IEEE, 1–4, https://doi.org/10.1109/VCIP.2017.8305148, 2017. a
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Short summary
We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.
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