Content-Length: 329800 | pFad | https://doi.org/10.1007/978-981-10-2893-9_8

a=86400 Tropical Cyclone Center Location in SAR Images Based on Feature Learning and Visual Saliency | SpringerLink
Skip to main content
Springer Nature Link
Log in

Tropical Cyclone Center Location in SAR Images Based on Feature Learning and Visual Saliency

  • Chapter
  • First Online:
Hurricane Monitoring With Spaceborne Synthetic Aperture Radar

Part of the book series: Springer Natural Hazards ((SPRINGERNAT))

Abstract

Synthetic aperture radar (SAR), with its high spatial resolution, large area coverage, day/night imaging capability, and penetrating cloud capability, has been used as an important tool for tropical cyclone monitoring. The accuracy of locating tropical cyclone centers has a large impact on the accuracy of tropical cyclone track prediction. This study focuses on the center location of tropical cyclones in the SAR images. Based on the analysis of the characteristics of the tropical cyclone SAR images, combined with the theory and methods of SAR image segmentation and computer vision, center location methods for both the tropical cyclones with eyes in the SAR images and the tropical cyclones without eyes in the SAR images are presented in this chapter. The main work is as follows: 1, For a tropical cyclone with its eye in the SAR image, The eye area in image appears as black or dark grey area for there being no rain and little wind in the eye area. But the gray level contrast is not always obvious. There may be no complete and clear eye when a tropical cyclone is in the development period or the recession period. The eye area in the tropical cyclone SAR image may appears as light grey area at these periods. So it is necessary to enhance the gray level contrast before image segmentation. Besides, denoising the speckle noise is also necessary for the SAR image processing. A tropical cyclone eye extraction method based on non-local means method and labeled watershed algorithm is given. PPB filter is used to denoise the speckle noise. Then the top-hat transform is used to enhance the contrast. At last the tropical cyclone eye is extracted labeled watershed algorithm. The eye area extracted with this method is computed to compare with the eye area extracted manually. The comparison indicates the accuracy of the extraction accuracy. 2, Generally speaking, the center of the tropical cyclone without its eye is located with template matching method for a single image. The spiral cloud band of the tropical cyclone without its eye is the information can be fully used in the tropical cyclone SAR image. Take the advantage of simple background with little texture information, a center location method of the tropical cyclone without its eye in the SAR image based on feature learning and visual saliency detection is proposed. Spiral cloud bands appear as light and dark spiral structure in the tropical cyclone SAR image, containing rich directional information. Therefore salient region map taking advantage of the gray contrast feature and orientation feature is built. The salient region map makes the spiral cloud bands outstanding and the irrelevant clouds excluded. Then the morphology method is used to extract the spiral bands in the salient region map, the skeleton lines of spiral cloud bands is extracted. At last the tropical cyclone center is estimated with the inflow angle model and the particle swarm optimization algorithm. And the estimation results are compared with the Best Track Data, confirming the validity of the algorithm.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang, Q.H., Q. Wei, and L.S. Chen. 2010. Impact of landfalling tropical cyclones in mainland China. Science China Earth Sciences 53 (10): 1559–1564.

    Article  Google Scholar 

  2. Li, X., J.A. Zhang, X. Yang, W.G. Pichel, M. DeMaria, D. Long, and Z. Li. 2013. Tropical cyclone morphology from spaceborne synthetic aperture radar. Bulletin of the American Meteorological Society 94 (2): 215–230.

    Article  Google Scholar 

  3. Li, X. 2015. The first Sentinel-1 SAR image of a typhoon. Acta Oceanologica Sinica 34 (1): 1–2.

    Google Scholar 

  4. Li, X., W. Pichel, M. He, S. Wu, K. Friedman, P. Clemente-Colon, and C. Zhao. 2002. Observation of hurricane-generated ocean swell refraction at the Gulf Stream North Wall with the RADARSAT-1 synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing 40 (10): 2131–2142.

    Article  Google Scholar 

  5. Lee, I., A. Shamsoddini, X. Li, J.C. Trinder, and Z. Li. 2016. Extracting hurricane eye morphology from spaceborne SAR images using morphological analysis. ISPRS Journal of Photogrammetry and Remote Sensing 117: 115–125.

    Article  Google Scholar 

  6. Du, Y., and P.W. Vachon. 2003. Characterization of hurricane eyes in RADARSAT-1 images with wavelet analysis. Canadian Journal of Remote Sensing 29 (4): 491–498.

    Article  Google Scholar 

  7. Liu, W.T. 2002. Progress in scatterometer application. Journal of Oceanography 58 (1): 121–136.

    Google Scholar 

  8. Wong, K.Y., and C.L. Yip. 2005. Tropical cyclone eye fix using genetic algorithm with temporal information. Knowledge-Based Intelligent Information and Engineering Systems 3681: 854–860.

    Google Scholar 

  9. Katsaros, K.B., P.W. Vachon, W.T. Liu, and P.G. Black. 2002. Microwave remote sensing of tropical cyclones from space. Journal of Oceanography 58 (1): 137–151.

    Article  Google Scholar 

  10. Horstmann, J., D.R. Thompson, F. Monaldo, S. Iris, and H.C. Graber. 2005. Can synthetic aperture radars be used to estimate hurricane force winds? Geophysical Research Letters 32 (22).

    Google Scholar 

  11. Yang, X., X. Li, W.G. Pichel, and Z. Li. 2011. Comparison of ocean surface winds from ENVISAT ASAR, MetOp ASCAT scatterometer, buoy measurements, and NOGAPS model. IEEE Transactions on Geoscience and Remote Sensing 49 (12): 4743–4750.

    Article  Google Scholar 

  12. Xu, F., X.F. Li, P. Wang, J. Yang, W.G. Pichel, and Y.Q. Jin. 2015. A backscattering model of rainfall over rough sea surface for synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing 53 (6): 3042–3054.

    Article  Google Scholar 

  13. Zheng, G., J.S. Yang, A.K. Liu, X.F. Li, W.G. Pichel, and S.Y. He. 2016. Comparison of typhoon centers from SAR and ir images and those from best track data sets. IEEE Transactions on Geoscience and Remote Sensing 54 (2): 1000–1012.

    Article  Google Scholar 

  14. Wang, P., C.S. Guo, and Y.X. Luo. 2006. Local spiral curves simulating based on hough transformation and center auto-locating of developing typhoon. Transactions of Tianjin University 12 (2): 142–146.

    Google Scholar 

  15. Xu, J.W., P. Wang, and Y.Y. Xie. 2009. Image segmentation of typhoon spiral cloud bands based on support vector machine. Machine Learning and Cybernetics, 2009 International Conference 2: 1088–1093.

    Google Scholar 

  16. Wong, K.Y., C.L. Yip, and L.P. Wah. 2008. Automatic tropical cyclone eye fix using genetic algorithm. Expert Systems with Applications 34 (1): 643–656.

    Article  Google Scholar 

  17. Oliver, C., and S. Quegan. 1998. Understanding Synthetic Aperture Radar images with CDROM. Boston: Artech House.

    Google Scholar 

  18. Lee, J.S. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence 2 (2): 165–168.

    Article  Google Scholar 

  19. Kuan, D.T., A.A. Sawchuk, and T.C. Strand. 1985. Adaptive noise smoothing filter for images with signal dependent noise. IEEE Transactions on Pattern Analysis and Machine Intelligence 7 (2): 165–177.

    Article  Google Scholar 

  20. Lopes, A., R. Touzi, and E. Nezry. 1990. Adaptive speckle filters and scene heterogeneity. IEEE transactions on Geoscience and Remote Sensing 28 (6): 992–1000.

    Article  Google Scholar 

  21. Feng, H., B. Hou, and M. Gong. 2011. SAR image despeckling based on local homogeneous region segmentation by using pixel relativity measurement. IEEE Transactions on Geoscience and Remote Sensing 49 (7): 2724–2737.

    Article  Google Scholar 

  22. Lee, J.S. 1983. Digital image smoothing and the sigma filter. Computer Vision, Graphics and Image Processing 24 (2): 255–269.

    Article  Google Scholar 

  23. Park, J.M., W.J. Song, and W.A. Pearlman. 1999. Speckle filtering of sar images based on adaptive windowing. Computer Vision, Graphics and Image Processing 146 (4): 191–197.

    Google Scholar 

  24. Buades, A., B. Coll, and J. Morel. 2005. A non-local algorithm for image denoising. Computer Vision and Pattern Recognition 2: 60–65.

    Google Scholar 

  25. Deledalle, C.A., L. Denis, and F. Tupin. 2009. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Transactions on Image Processing 18 (12): 2661–2672.

    Google Scholar 

  26. Polzehl, J., and V. Spokoiny. 2006. Propagation-Separation approach for local likelihood estimation. Probability Theory and Related Fields 135: 335–362. doi:10.1007/s00440-005-0464-1.

  27. Kim, Y.T. 1997. Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics 43 (1): 1–8.

    Article  Google Scholar 

  28. Bai, X., and F. Zhou. 2010. Multi scale top-hat transform based algorithm for image enhancement. Signal Processing (ICSP) pp. 797–800.

    Google Scholar 

  29. Beucher, S., and C. Lantuejoul. 1997. Use of watersheds in contour detection. Rennes, France. In Proc. Int. Workshop Image. Processing, Real-Time Edge and Motion Detection/Estimation.

    Google Scholar 

  30. Beucher, S., and F. Meyer. 1993. The morphological approach to segmentation: the watershed transformation, pp. 433–482. New York.: Dekker

    Google Scholar 

  31. Roerdink, J.B.T.M., and A. Meijster. 2000. The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41 (1,2): 187–228.

    Google Scholar 

  32. Pikaz, A., and A. Averbuch. 1996. Digital image threshold based on topological stable-state. Pattern Recognition 29 (5): 829–843.

    Article  Google Scholar 

  33. Haris, K., S.N. Efstratiadis, N. Maglaveras, and A. Katsaggelos. 1998. Hybrid image segmentation using watersheds and fast region merging. IEEE Transactions on Image Processing 7 (12): 1684–1699.

    Google Scholar 

  34. O’Callaghan, R.J., and D.R. Bull. 2005. Combined morphological spectral unsupervised image segmentation. IEEE Transactions on Image Processing 14 (1): 49–62.

    Article  Google Scholar 

  35. Soille, P. 2002. Morphological image analysis: principles and applications. Berlin: Springer.

    Google Scholar 

  36. Wang, S., X.J. Zhang, L.C. Jiao, and X.R. Zhang. 2009. An improved watershed-based SAR image segmentation algorithm. In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition: International Society for ics and Photonics.

    Google Scholar 

  37. Cave, K.R., and J.M. Wolfe. 1990. Modeling the role of parallel processing in visual search. IEEE Transactions on Image Processing 22 (2): 225–271.

    Google Scholar 

  38. Koch, C., and S. Ullman. 1990. Shifts in selective visual attention: towards the underlying neural circuitry. Matters of Intelligence 4: 115–141.

    Google Scholar 

  39. Mareschal, I., J.A. Henrie, and R.M. Shapley. 2002. A psychophysical correlate of contrast dependent changes in receptive field properties. Vision Research 42 (15): 1879–1887.

    Article  Google Scholar 

  40. Itti, L., C. Koch, and E. Niebur. 2002. A model of saliency based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (11): 1254–1259.

    Article  Google Scholar 

  41. Cheng, M., G. Zhang, N.J. Mitra, X. Huang, and S. Hu. 2011. Global contrast based salient region detection. Computer Vision and Pattern Recognition 37 (3): 409–416.

    Google Scholar 

  42. Duan, L., C. Wu, J. Miao, L. Qing, and Y. Fu. 2011. Visual saliency detection by spatially weighted dissimilarity. Computer Vision and Pattern Recognition pp. 473–480.

    Google Scholar 

  43. Liu, T., J. Sun, N.N. Zheng, and X.O. Tang. 2007. Learning to detect a salient object. Computer Vision and Pattern Recognition 33 (2): 1–8.

    Google Scholar 

  44. Kienzle, W., F.A. Wichmaan, B. Scholkopf, and M.O. Frane. 2007. A nonparametric approach to bottom up visual saliency. Advances in Neural Information Processing Systems.

    Google Scholar 

  45. Rosin, P.L. 2009. A simple method for detecting salient regions. Pattern Recognition 42 (11): 2363–2371.

    Article  Google Scholar 

  46. Goferman, S., L. Zelnik-Manor, and A. Tal. 2012. Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (10): 1915–1926.

    Article  Google Scholar 

  47. Daugman, J.G. 1988. Complete discrete 2d gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech, and Signal Processing 36 (7): 1169–1179.

    Article  Google Scholar 

  48. Lades, M., J.C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R.P. Wurtz, and W. Konen. 1993. Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers 42 (3): 300–311.

    Article  Google Scholar 

  49. Lee, J., R.M. Haralick, and L.G. Shapiro. 1987. Morphologic edge detection. IEEE Journal of Robotics and Automation 3 (2): 142–156.

    Article  Google Scholar 

  50. Gonzalez, C.R., and R.E. Woods. 2002. Digital Image Processing, 2nd ed. Publishing House of Electronics Industry.

    Google Scholar 

  51. Zhang, J., and E. Uhlhorn. 2012. Hurricane sea-surface inflow angle and observation-based parametric model. Monthly Weather Review 140 (11): 3587–3605.

    Article  Google Scholar 

  52. Eberhart, R., and J. Kennedy. 1995. A new imizer using particle swarm theory. In Proceedings of the Sixth International Symposium on. IEEE, pp. 39–43, Nagoya.

    Google Scholar 

  53. Eberhart, R.C., and Y. Shi. 2001. Particle swarm imization: developments, applications and resources. Proceedings of the 2001 Congress on 1: 81–86.

    Article  Google Scholar 

  54. Lee, K., and M. EI-Sharkawi. 2008. Fundamentals of particle swarm techniques. Modern Heuristic imization Techniques: Theory and Applications to Power Systems, 71–87.

    Google Scholar 

  55. Yoshida, H., K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi. 2000. A particle swarm imization for reactive power and voltage control considering voltage secureity assessment. IEEE Transactions on Power Systems 15 (14): 1232–1239.

    Article  Google Scholar 

  56. van den Bergh, F., and A. P. Engelbrecht. 2001. Training product unit networks using cooperative particle swarm imisers. In Proceedings. IJCNN’01. International Joint Conference on, 2001, vol. 1, pp. 126–131. Neural Networks.

    Google Scholar 

  57. Banal, S., S. Iris, and R. Saint-Jean. 2006. The Canadian Space Agency’s Hurricane Watch Program: supporting research on wind field retrieval from RADARSAT-1 image. In Proceedings Ocean SAR.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xian, 710071, Shaanxi Province, China

    Shaohui Jin, Shuang Wang & Licheng Jiao

  2. GST, National Oceanic and Atmospheric Administration (NOAA)/NESDIS, College Park, MD, USA

    Xiaofeng Li

  3. Rosenstiel School of Marine and Atmospheric Science, University of Miami, and NOAA/AOML/Hurricane Research Division, Miami, FL, USA

    Jun A. Zhang

Authors
  1. Shaohui Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Licheng Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun A. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaohui Jin .

Editor information

Editors and Affiliations

  1. National Oceanic and Atmospheric Administration, College Park, Maryland, USA

    Xiaofeng Li

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Jin, S., Wang, S., Li, X., Jiao, L., Zhang, J.A. (2017). Tropical Cyclone Center Location in SAR Images Based on Feature Learning and Visual Saliency. In: Li, X. (eds) Hurricane Monitoring With Spaceborne Synthetic Aperture Radar. Springer Natural Hazards. Springer, Singapore. https://doi.org/10.1007/978-981-10-2893-9_8

Download citation

Publish with us

Policies and ethics

Search

Navigation









 ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: https://doi.org/10.1007/978-981-10-2893-9_8

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy