Skip to main content
SpringerLink
Log in

A real-time typhoon eye detection method based on deep learning for meteorological information forensics

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

The development of meteorological satellite technology has made it feasible to observe cloud cover over the Earth’s surface, and the number of high-precision meteorological satellite images available has increased dramatically over the years. However, there exists a gap between meteorological satellite cloud images and the true information of the pictured clouds. Therefore, extracting the true atmospheric information from “forged” satellite images in real time is a challenging task. In this paper, we proposed a real-time typhoon eye detection method from meteorological satellite cloud images based on deep learning. This new approach is the first step in detecting hidden information in satellite cloud images and provides important data support to detect true typhoon information. We performed simulation experiments and the results showed that the proposed method performs well in identifying typhoons, where the positive sample accuracy rate, negative sample accuracy rate, and total average accuracy rate are 94.22%, 99.43%, and 96.83%, respectively. In the testing process, the average time needed to detect each sample is 6 ms, which fulfills the requirement for real-time typhoon eye detection. Our method outperforms the k-nearest neighbors (KNN) and support vector machine (SVM) algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Zhang, W., Cui, X.P.: Review of tropical cyclone generation. J. Trop. Meteorol. 29(2), 337–346 (2013)

    Google Scholar 

  2. Jin, L.: Progress in researches on artificial neural network theory and application in atmospheric science. Meteorol. Sci. Technol. 32(6), 385–392 (2004)

    Google Scholar 

  3. Shi, C.X., Wu, R.Z., Xiang, X.K.: Automatic segmentation of satellite image using hierarchical threshold and neural network. Q. J. Appl. Meteorol. 12(1), 70–78 (2001)

    Google Scholar 

  4. Zheng, J.J., Huang, F., Zhang, Y.S.: Research of cloud identification based on neural network. Comput. Eng. 30(18), 24–25 (2004)

    Google Scholar 

  5. Li, W.L.: Research on the recognition method of typhoon during its developing stage. Tianjin University (2003)

  6. Vannoorenberghe, P., Flouzat, G.: A belief-based pixel labeling strategy for medical and satellite image segmentation. In: IEEE international conference on fuzzy systems, IEEE, pp. 1093–1098 (2006)

  7. Ooi, W.S., Lim, C.P.: Fuzzy clustering of color and texture features for image segmentation: a study on satellite image retrieval. J. Intell. Fuzzy Syst. 17(3), 297–311 (2006)

    Google Scholar 

  8. Intajag, S., Paithoonwatanakij, K., Cracknell, A.P.: Iterative satellite image segmentation by fuzzy hit-or-miss and homogeneity index. IEE Proc. Vis. Image Signal Process. 153(2), 206–214 (2006)

    Article  Google Scholar 

  9. Bao, C.L., Ma, W.M., Chen, W.M.: Satellite cloud maps study on formation and track of typhoon moving northward. J. Nat. Disasters 10(2), 12–18 (2001)

    Google Scholar 

  10. Ma, W.M., Wu, X.T., Chen, X.X.: Satellite cloud maps’ features of typhoon formation. Mar. Forecasts 17(3), 1–10 (2000)

    Google Scholar 

  11. Zeng, M.J., Yu, B., Zhou, Z.K.: Researches and applications of locating technology for typhoon center on satellite infrared cloud picture. J. Trop. Meteorol. 22(03), 35–41 (2006)

    Google Scholar 

  12. Kovordányi, R., Roy, C.: Cyclone track forecasting based on satellite images using artificial neural networks. ISPRS J. Photogramm. Remote Sens. 64(6), 513–521 (2009)

    Article  Google Scholar 

  13. Lu, J., Zhang, C.J., Zhang, X.: Auto-recognition of typhoon cloud based on boundary features. J. Remote Sens. 14(5), 990–1003 (2010)

    Google Scholar 

  14. Wang, Y., Sun, J.-D., Han, Y., et al.: Segmentation of tropical cyclone cloud systems with compositive algorithms. Remote Sens. Technol. Appl. 26(3), 287–293 (2011)

    Google Scholar 

  15. Rüttgers, M., Lee, S., You, D.: Typhoon track prediction using satellite images in a Generative Adversarial Network. arXiv:1808.05382 (2018)

  16. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  Google Scholar 

  17. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  18. Fang, W., Zhang, F., Sheng, V.S., Ding, Y.: A method for improving CNN-based image recognition using DCGAN. Comput. Mater. Continua 57(1), 167–178 (2018)

    Article  Google Scholar 

  19. Zheng, Y., Jeon, B., Sun, L., Zhang, J., Zhang, H.: Student’s t-hidden Markov model for Unsupervised learning using localized feature selection. IEEE Trans. Circ. Syst. Video Technol. 28(10), 2586–2598 (2017)

    Article  Google Scholar 

  20. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)

    Article  Google Scholar 

  21. Cliche, M.: BB_twtr at SemEval-2017 Task 4: twitter sentiment analysis with CNNs and LSTMs. In: Proceedings of the 11th international workshop on semantic evaluation, pp 573–580 (2017)

  22. Christiansen, E.M., Yang, S.J., Ando, D.M., Javaherian, A., Skibinski, G., Lipnick, S., Mount, E., O’Neil, A., Shah, K., Lee, A.K., Goyal, P., Fedus, W., Poplin, R., Esteva, A., Berndl, M., Rubin, L.L., Nelson, P., Finkbeiner, S.: In silico labeling: predicting fluorescent labels in unlabeled images. Cell. 173(3), 792–803 (2018)

    Article  Google Scholar 

  23. Tu, Y., Lin, Y., Wang, J., Kim, J.-U.: Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput. Mater. Continua 55(2), 243–254 (2018)

    Google Scholar 

  24. Zheng, Y., Sun, L., Wang, S., Zhang, J., Ning, J.: Spatially regularized structural support vector machine for robust visual tracking. IEEE Trans. Neural Netw. Learn. Syst. (2018). https://doi.org/10.1109/TNNLS.2018.2855686

    Article  Google Scholar 

  25. Yu, S., Wu, Y., Li, W., Song, Z., Zeng, W.: A model for fine-grained vehicle classification based on deep learning. Neurocomputing 257, 97–103 (2017)

    Article  Google Scholar 

  26. Zhou, S., Liang, W., Li, J., Kim, J.-U.: Improved VGG model for road traffic sign recognition. Comput. Mater. Continua 57(1), 11–24 (2018)

    Article  Google Scholar 

  27. Wenwen, G., Lianyong, Q., Yanwei, X.: Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment. Wirel. Commun. Mobile Comput. 2018, 8 (2018)

    Google Scholar 

  28. Lianyong, Q., Xiaolong, X., Wanchun, D., Jiguo, Y., Zhili, Z., Xuyun, Z.: Time-aware IoE service recommendation on sparse data. Mobile Inform. Syst. 2016, 12 (2016)

    Google Scholar 

  29. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2015). arXiv:1412.6980

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant no. 61802199) and the Student Practice Innovation Training Program Fund of the Nanjing University of Information Science and Technology (Grant no. 2017103000170).

Author information

Authors and Affiliations

  1. School of Automation, Nanjing University of Information Science and Technology, 219, Ningliu Road, Nanjing, 210044, China

    Liling Zhao

  2. Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing, 210044, Jiangsu, China

    Liling Zhao

  3. Binjiang College, Nanjing University of Information Science and Technology, 333, Xishan Road, Wuxi, 214105, China

    Yifei Chen

  4. Computer Science Department, University of Central Arkansas, Conway, AR, 72035, USA

    Victor S. Sheng

Authors
  1. Liling Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yifei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor S. Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liling Zhao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, L., Chen, Y. & Sheng, V.S. A real-time typhoon eye detection method based on deep learning for meteorological information forensics. J Real-Time Image Proc 17, 95–102 (2020). https://doi.org/10.1007/s11554-019-00899-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-019-00899-2

Keywords

Search

Navigation