Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2016, Article ID 4129708, 13 pages
http://dx.doi.org/10.1155/2016/4129708
Research Article

Anomalous Propagation Echo Classification of Imbalanced Radar Data with Support Vector Machine

Department of Electrical and Computer Engineering, Pusan National University, Busan 46241, Republic of Korea

Received 23 September 2015; Revised 30 November 2015; Accepted 10 January 2016

Academic Editor: Brian R. Nelson

Copyright © 2016 Hansoo Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. E. Rinehart, Radar for Meteorologists, University of North Dakota, Office of the President, 1991.
  2. Met Office, National Meteorological Library and Archive Fact Sheet No. 15—Weather Radar, 2015, http://www.metoffice.gov.uk/learning/library/publications/factsheets.
  3. L. Rosenberg, “Sea-spike detection in high grazing angle X-band sea-clutter,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 8, pp. 4556–4562, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Haykin, R. Barker, and B. W. Currie, “Uncovering nonlinear dynamics—the case study of sea clutter,” Proceedings of the IEEE, vol. 90, no. 5, pp. 860–881, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. V. Lakshmanan, A. Fritz, T. Smith, K. Hondl, and G. Stumf, “An automated technique to quality control radar reflectivity data,” Journal of Applied Meteorology and Climatology, vol. 46, no. 3, pp. 288–305, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. H. Kim, S. Kim, H.-Y. Han, B.-H. Heo, and C.-H. You, “Real-time detection and filtering of chaff clutter from single-polarization doppler radar data,” Journal of Atmospheric and Oceanic Technology, vol. 30, no. 5, pp. 873–895, 2013. View at Google Scholar
  7. X. Shao, H. Du, and J. Xue, “A new method of ship and chaff polarization recognition under rain and snow cluster,” in Proceedings of the IEEE International Workshop on Anti-counterfeiting, Security, Identification (ASID '07), pp. 142–147, IEEE, Fujian, China, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Moszkowicz, G. J. Ciach, and W. F. Krajewski, “Statistical detection of anomalous propagation in radar reflectivity patterns,” Journal of Atmospheric and Oceanic Technology, vol. 11, no. 4, pp. 1026–1034, 1994. View at Google Scholar · View at Scopus
  9. F. Mesnard and H. Sauvageot, “Climatology of anomalous propagation radar echoes in a coastal area,” Journal of Applied Meteorology and Climatology, vol. 49, no. 11, pp. 2285–2300, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Neuper and J. Handwerker, Anomalous Propagation: Examination of Ducting Conditions and Anaprop Events in SW-Germany, Seminar Work, Kalsruhe Insititute of Technology, 2010.
  11. J. Bech, A. Sairouni, B. Codina, J. Lorente, and D. Bebbington, “Weather radar anaprop conditions at a Mediterranean coastal site,” Physics and Chemistry of the Earth Part B: Hydrology, Oceans and Atmosphere, vol. 25, no. 10–12, pp. 829–832, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. J. A. Pamment and B. J. Conway, “Objective identification of echoes due to anomalous propagation in weather radar data,” Journal of Atmospheric and Oceanic Technology, vol. 15, no. 1, pp. 98–113, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. J. R. Peter, A. Seed, and P. J. Steinle, “Application of a bayesian classifier of anomalous propagation to single-polarization radar reflectivity data,” Journal of Atmospheric and Oceanic Technology, vol. 30, no. 9, pp. 1985–2005, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. R. B. Da Silveira and A. R. Holt, “An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 8, pp. 1777–1788, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Grecu and W. F. Krajewski, “An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks,” Journal of Atmospheric and Oceanic Technology, vol. 17, no. 2, pp. 121–129, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Grecu and W. F. Krajewski, “Detection of anomalous propagation echoes in weather radar data using neural networks,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 1, pp. 287–296, 1999. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Berenguer, D. Sempere-Torres, C. Corral, and R. Sánchez-Diezma, “A fuzzy logic technique for identifying nonprecipitating echoes in radar scans,” Journal of Atmospheric and Oceanic Technology, vol. 23, no. 9, pp. 1157–1180, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. Y.-H. Cho, G. Lee, K.-E. Kim, and I. Zawadzki, “Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes,” Journal of Atmospheric and Oceanic Technology, vol. 23, no. 9, pp. 1206–1222, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. B. Haddad, A. Adane, F. Mesnard, and H. Sauvageot, “Modeling anomalous radar propagation using first-order two-state Markov chains,” Atmospheric Research, vol. 52, no. 4, pp. 283–292, 1999. View at Publisher · View at Google Scholar · View at Scopus
  20. M. A. Rico-Ramirez and I. D. Cluckie, “Classification of ground clutter and anomalous propagation using dual-polarization weather radar,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 7, pp. 1892–1904, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. W. F. Krajewski and B. Vignal, “Evaluation of anomalous propagation echo detection in WSR-88D data: a large sample case study,” Journal of Atmospheric and Oceanic Technology, vol. 18, no. 5, pp. 807–814, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, Mass, USA, 2001.
  23. T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data,” Bioinformatics, vol. 16, no. 10, pp. 906–914, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998. View at Publisher · View at Google Scholar · View at Scopus
  25. C.-F. Lin and S.-D. Wang, “Fuzzy support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 464–471, 2002. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Lee and E. Kim, “Genetic outlier detection for a robust support vector machine,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 2, pp. 96–101, 2015. View at Publisher · View at Google Scholar
  27. S.-Y. Lee, D. Ahn, M. Song, and K. Lee, “The classification of electrocardiograph arrhythmia patterns using fuzzy support vector machines,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 11, no. 3, pp. 204–210, 2011. View at Publisher · View at Google Scholar
  28. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. View at Google Scholar · View at Scopus
  29. P. Jeatrakul, K. W. Wong, and C. C. Fung, “Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm,” in Neural Information Processing. Models and Applications: 17th International Conference, ICONIP 2010, Sydney, Australia, November 22–25, 2010, Proceedings, Part II, vol. 6444, pp. 152–159, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  30. J. Wang, M. Xu, H. Wang, and J. Zhang, “Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding,” in Proceedings of the 8th International Conference on Signal Processing (ICSP '06), vol. 3, IEEE, Beijing, China, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Heistermann, S. Jacobi, and T. Pfaff, “Technical Note: an open source library for processing weather radar data (wradlib),” Hydrology and Earth System Sciences, vol. 17, no. 2, pp. 863–871, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. Tropical Rainfall Measuring Mission, NASA, September 2015, http://trmm-fc.gsfc.nasa.gov/trmm_gv/software/rsl/.
  33. J. E. Nielsen, S. Thorndahl, and M. R. Rasmussen, “Improving weather radar precipitation estimates by combining two types of radars,” Atmospheric Research, vol. 139, pp. 36–45, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Categorical, “Glossary of terms,” Machine Learning, vol. 30, no. 2, pp. 271–274, 1998. View at Publisher · View at Google Scholar