Abstract
Severe convective weather is a catastrophic weather that can cause great harm to the public. One of the key studies for meteorological practitioners is how to recognize severe convection weather accurately and effectively, and it is also an important issue in government climate risk management. However, most existing methods extract features from satellite data by classifying individual pixels instead of using tightly integrated spatial information, ignoring the fact the clouds are highly dynamic. In this paper, we propose a new classification model, which is based on image segmentation of deep learning. And it uses U-net architecture as the technology platform to identify all weather conditions in the datasets accurately. As heavy rainfall is one of the most frequent and widespread server weather hazards, when the storms come ashore with high speed of wind, it makes the precipitation time longer and causes serious damage in turn. Therefore, we suggest a new evaluation metric to evaluate the performance of detecting heavy rainfall. Compared with existing methods, the model based on Himawari-8 dataset has a better performance. Further, we explore the representations learned by our model in order to better understand this important dataset. The results play a crucial role in the prediction of climate change risks and the formulation of government policies on climate change.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim, D.H., Ahn, M.H.: Introduction of the in-orbit test and its performance for the first meteorological imager of the Communication, Ocean, and Meteorological Satellite. Atmos. Measur. Tech. 7(8), 2471–2485 (2014). https://doi.org/10.5194/amt-7-2471-2014
Moradi Kordmahalleh, M., Gorji Sefidmazgi, M., Homaifar, A.: A sparse recurrent neural network for trajectory prediction of atlantic hurricanes. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO 2016. ACM Press (2016). https://doi.org/10.1145/2908812.2908834
Tan, C., et al.: FORECAST-CLSTM: a new convolutional LSTM network for cloudage nowcasting. In: 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, December 2018. https://doi.org/10.1109/vcip.2018.8698733
Shi, M., et al.: Cloud detection of remote sensing images by deep learning. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, July 2016. https://doi.org/10.1109/igarss.2016.7729176
Jedlovec, G.J., Haines, S.L., LaFontaine, F.J.: Spatial and temporal varying thresholds for cloud detection in GOES imagery. IEEE Trans. Geosci. Remote Sens. 46(6), 1705–1717 (2008). https://doi.org/10.1109/tgrs.2008.916208
Kurth, T., et al.: Exascale deep learning for climate analytics. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, November 2018. https://doi.org/10.1109/sc.2018.00054
Jegou, S., et al.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, July 2017. https://doi.org/10.1109/cvprw.2017.156
Kong, H., Fan, L., Zhang, X.: Semantic segmentation with inverted residuals and atrous convolution. In: SAE Technical Paper Series. SAE International, August 2018. https://doi.org/10.4271/2018-01-1635
Filipcic, A., et al.: ATLAS computing on CSCS HPC. J. Phys.: Conf. Ser. 664(9), 092011 (2015). https://doi.org/10.1088/1742-6596/664/9/092011
Hines, J.: Stepping up to summit. Comput. Sci. Eng. 20(2), 78–82 (2018). https://doi.org/10.1109/mcse.2018.021651341
Yuan, Y., Hu, X.: Bag-of-words and object-based classification for cloud extraction from satellite imagery. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 8(8), 4197–4205 (2015). https://doi.org/10.1109/jstars.2015.2431676
Racah, E., et al.: Extremeweather: a large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In: Advances in Neural Information Processing Systems, pp. 3402–3413 (2017)
Hong, S., et al.: GlobeNet: convolutional neural networks for typhoon eye tracking from remote sensing imagery (2017)
Liu, H., Zeng, D., Tian, Q.: Super-pixel cloud detection using hierarchical fusion CNN. In: 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM). IEEE, September 2018. https://doi.org/10.1109/bigmm.2018.8499091
Nahler, G.: Pearson correlation coefficient. In: Dictionary of Pharmaceutical Medicine, pp. 132–132. Springer, Vienna (2009). https://doi.org/10.1007/978-3-211-89836-9_1025
Ronneberger, O.: Invited talk: U-Net convolutional networks for biomedical image segmentation. Bildverarbeitung für die Medizin 2017. I, p. 3. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54345-0_3
Berger, L., Eoin, H., Cardoso, M.J., Ourselin, S.: An adaptive sampling scheme to efficiently train fully convolutional networks for semantic segmentation. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds.) MIUA 2018. CCIS, vol. 894, pp. 277–286. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95921-4_26
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017). https://doi.org/10.1109/tpami.2016.2644615
Chen, T., et al.: Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)
Lateef, F., Ruichek, Y.: Survey on semantic segmentation using deep learning techniques. Neurocomputing 338, 321–348 (2019). https://doi.org/10.1016/j.neucom.2019.02.003
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, M., Chai, Z., Zhao, W. (2019). Severe Convective Weather Classification in Remote Sensing Images by Semantic Segmentation. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-30508-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30507-9
Online ISBN: 978-3-030-30508-6
eBook Packages: Computer ScienceComputer Science (R0)