Journal of Ocean University of China

, Volume 17, Issue 1, pp 118–128 | Cite as

Intelligent identification of remnant ridge edges in region west of Yongxing Island, South China Sea

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Abstract

Edge detection enables identification of geomorphologic unit boundaries and thus assists with geomorphical mapping. In this paper, an intelligent edge identification method is proposed and image processing techniques are applied to multi-beam bathymetry data. To accomplish this, a color image is generated by the bathymetry, and a weighted method is used to convert the color image to a gray image. As the quality of the image has a significant influence on edge detection, different filter methods are applied to the gray image for de-noising. The peak signal-to-noise ratio and mean square error are calculated to evaluate which filter method is most appropriate for depth image filtering and the edge is subsequently detected using an image binarization method. Traditional image binarization methods cannot manage the complicated uneven seafloor, and therefore a binarization method is proposed that is based on the difference between image pixel values; the appropriate threshold for image binarization is estimated according to the probability distribution of pixel value differences between two adjacent pixels in horizontal and vertical directions, respectively. Finally, an eight-neighborhood frame is adopted to thin the binary image, connect the intermittent edge, and implement contour extraction. Experimental results show that the method described here can recognize the main boundaries of geomorphologic units. In addition, the proposed automatic edge identification method avoids use of subjective judgment, and reduces time and labor costs.

Key words

geomorphology mapping edge detection image processing 
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Notes

Acknowledgements

We acknowledge Guangzhou Marine Geological Survey for providing permission to release the bathy-metric map. This work was supported by the National Natural Science Foundation of China (Nos. 41576049, 41666002), the Key Research Projects of Frontier Science of the Chinese Academy of Sciences (No. QYZDB-SSW-SYS025), Geological projects of China Geological Survey (Nos. GZH 201400210, DD20160140), the Natural Science Foundation of Hainan (No. ZDYF2016215), and the Key Science and Technology Foundation of Sanya (Nos. 2017PT13, 2017 PT14). We express our sincere gratitude to the two anonymous reviewers for their valuable comments, which greatly improved this manuscript.

References

  1. Anagnostopoulos, C. N. E., Anagnostopoulos, I. E., Loumos, V., and Kayafas, E., 2006. A license plate-recognition algorithm for intelligent transportation system applications. IEEE Transactions on Intelligent Transportation Systems, 7 (3): 377–392.CrossRefGoogle Scholar
  2. Bao, P., Zhang, L., and Wu, X., 2005. Canny edge detection enhancement by scale multiplication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (9): 1485–1490.CrossRefGoogle Scholar
  3. Canny, J., 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8 (6): 679–698.CrossRefGoogle Scholar
  4. Carter, R. C., Gani, M. R., Roesler, T., and Sarwar, A. K., 2016. Submarine channel evolution linked to rising salt domes, Gulf of Mexico, USA. Sedimentary Geology, 342: 237–253.CrossRefGoogle Scholar
  5. Chen, H., Peng, X., Zhu, B., Zhong, H., Huang, Y., Sun, G., Wang, L., and Guo, L., 2014. A brief review of 1:1000000 marine geological survey and mapping results of the Hainan sheet in the South China Sea. Marine Geology & Quaternary Geology, 34 (6): 83–96 (in Chinese with English abstract).Google Scholar
  6. Chen, T., Ma, K., and Chen, L., 1999. Tri-state median filter for image denoising. IEEE Transactions on Image Processing, 8 (12): 1834–1838.CrossRefGoogle Scholar
  7. Costello, M. J., Cheung, A., and De Hauwere, N., 2010. Surface area and the seabed area, volume, depth, slope, and topographic variation for the world’s seas, oceans, and countries. Environmental Science & Technology, 44 (23): 8821–8828.CrossRefGoogle Scholar
  8. Eng, H., and Ma, K., 2001. Noise adaptive soft-switching median filter. IEEE Transactions on Image Processing, 10 (2): 242–251.CrossRefGoogle Scholar
  9. Foss, J. H., Lindberg, B. R., Christensen, O., Lundälv, T., Svellingen, I., Mortensen, P. L. B., and Alvsvag, J., 2005. Mapping of Lophelia reefs in Norway: Experiences and survey methods. In: Cold-water Corals and Ecosystems. Springer Berlin Heidelberg, Berlin, 359–391.CrossRefGoogle Scholar
  10. Guo, Z., and Hall, R. W., 1989. Parallel thinning with twosubiteration algorithms. Communications of the Acm, 32 (3): 359–373.CrossRefGoogle Scholar
  11. Harris, P. T., and Whiteway, T., 2011. Global distribution of large submarine canyons: Geomorphic differences between active and passive continental margins. Marine Geology, 285 (1–4): 69–86.CrossRefGoogle Scholar
  12. Harris, P. T., Macmillan-Lawler, M., Rupp, J., and Baker, E. K., 2014. Geomorphology of the oceans. Marine Geology, 352: 4–24.CrossRefGoogle Scholar
  13. He, K., Sun, J., and Tang, X., 2013. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (6): 1397–1409.CrossRefGoogle Scholar
  14. Heap, A. D., and Harris, P. T., 2008. Geomorphology of the Australian margin and adjacent seafloor. Australian Journal of Earth Sciences, 55 (4): 555–585.CrossRefGoogle Scholar
  15. Hore, A., and Ziou, D., 2010. Image quality metrics: PSNR vs. SSIM. The 20th International Conference on Pattern Recognition. Washington, D C, 2366–2369.Google Scholar
  16. Howe, J. A., Anderton, R., Arosio, R., Dove, D., Bradwell, T., Crump, P., Cooper, R., and Cocuccio, A., 2015. The seabed geomorphology and geological structure of the firth of Lorn, western Scotland, UK, as revealed by multibeam echosounder survey. Earth and Environmental Science Transactions of the Royal Society of Edinburgh, 105 (4): 273–284.CrossRefGoogle Scholar
  17. Hwang, H., and Haddad, R. A., 1995. Adaptive median filters: New algorithms and results. IEEE Transactions on Image Processing, 4 (4): 499–502.CrossRefGoogle Scholar
  18. Jin, M., Jin, W., Li, Y., and Li, S., 2016. An evaluation method based on absolute difference to validate the performance of SBNUC algorithms. Infrared Physics & Technology, 78: 1–12.CrossRefGoogle Scholar
  19. Kim, T., and Paik, J., 2008. Adaptive contrast enhancement using gain-controllable clipped histogram equalization. IEEE Transactions on Consumer Electronics, 54 (4): 1803–1010.CrossRefGoogle Scholar
  20. Li, M. Y., and Chen, G. X., 2015. Research on gray balance in ink-jet printing based on paper color characteristic. Applied Mechanics and Materials, 731: 308–311.CrossRefGoogle Scholar
  21. Liu, Y., Lan, C., Li, C., Mo, F., and Wang, H., 2016. S-AKAZE: An effective point-based method for image matching. Optik–International Journal for Light and Electron Optics, 127 (14): 5670–5681.CrossRefGoogle Scholar
  22. Luoh, T., Lv, R., Chen, C., Liao, H., Yang, L., Yang, T., Chen, K., and Lu, C., 2014. Systematic hot spots finding by pattern search with similarity. ECS Transactions, 60 (1): 279–283.CrossRefGoogle Scholar
  23. Ma, J. L., and Zhang, X., 2013. Method for uncooled infrared image processing. Applied Mechanics and Materials, 427–429: 1948–1951.CrossRefGoogle Scholar
  24. Melin, P., Gonzalez, C. I., Castro, J. R., Mendoza, O., and Castillo, O., 2014. Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Transactions on Fuzzy Systems, 22 (6): 1515–1525.CrossRefGoogle Scholar
  25. Mythili, C., and Kavitha, V., 2011. Efficient technique for color image noise reduction. The Research Bulletin of Jordan ACM, II (III): 41–44.Google Scholar
  26. Nadernejad, E., Sharifzadeh, S., and Hassanpour, H., 2008. Edge detection techniques: Evaluations and comparison. Applied Mathematical Sciences, 2 (31): 1507–1520.Google Scholar
  27. Ng, P., and Ma, K., 2006. A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing, 15 (6): 1506–1516.CrossRefGoogle Scholar
  28. Pal, A. B., Dubey, A. K., and Chaturvedi, A., 2016. Shrinkage estimation in spatial autoregressive model. Journal of Multivariate Analysis, 143: 362–373.CrossRefGoogle Scholar
  29. Pavlidis, T., 1982. Algorithms for Graphics and Image Processing. Springer-Verlag Berlin Heidelberg, New York, 1–448.CrossRefGoogle Scholar
  30. Prior, D. B., and Hooper, J. R., 1999. Sea floor engineering geomorphology: Recent achievements and future directions. Geomorphology, 31 (1): 411–439.CrossRefGoogle Scholar
  31. Qiu, X., Ye, S., Wu, S., Shi, X., Zhou, D., Xia, K., and Flueh, E. R., 2001. Crustal structure across the Xisha Trough, northwestern South China Sea. Tectonophysics, 341 (1): 179–193.CrossRefGoogle Scholar
  32. Raghavan, V., Masumoto, S., Koike, K., and Nagano, S., 1995. Automatic lineament extraction from digital images using a segment tracing and rotation transformation approach. Computers & Geosciences, 21 (4): 555–591.CrossRefGoogle Scholar
  33. Rong, W., Li, Z., Zhang, W., and Sun, L., 2014. An improved Canny edge detection algorithm. 2014 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China.Google Scholar
  34. Rusu, A., and Voicu, N. F., 2016. A survey on edge detection in images. Journal of Information Systems & Operations Management, 10 (1): 1–13.CrossRefGoogle Scholar
  35. Sauvola, J., and Pietikäinen, M., 2000. Adaptive document image binarization. Pattern Recognition, 33 (2): 225–236.CrossRefGoogle Scholar
  36. Shi, X., Zhou, D., Qiu, X., and Zhang, Y., 2002. Thermal and rheological structures of the Xisha Trough, South China Sea. Tectonophysics, 351 (4): 285–300.CrossRefGoogle Scholar
  37. Smith, W. H., and Sandwell, D. T., 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277 (5334): 1956–1962.CrossRefGoogle Scholar
  38. Sun, C. Y., Wu, N. Y., Niu, B. H., Sun, Y., Cui, Y., Luo, W., and Chen, J. P., 2007. Geochemical characteristics of gaseous hydrocarbons and hydrate resource prediction in the Qiongdongnan Basin of the South China Sea. Geoscience, 21 (1): 95–100 (in Chinese with English abstract).Google Scholar
  39. Suzuki, S., 1985. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30 (1): 32–46.CrossRefGoogle Scholar
  40. Taylor, B., and Hayes, D. E., 1983. Origin and history of the South China Sea. In: The Tectonics and Geological Evolution of Southeast Asia Seas and Islands: Part 2. Hayes, D. E., ed., American Geophysical Union, Washington, DC, 23–56.CrossRefGoogle Scholar
  41. Völker, D., Scholz, F., and Geersen, J., 2011. Analysis of submarine landsliding in the rupture area of the 27 February 2010 Maule earthquake, central Chile. Marine Geology, 288 (1–4): 79–89.CrossRefGoogle Scholar
  42. Wang, D., Wu, S., Yao, G., and Wang, W., 2015. Architecture and evolution of deep-water cyclic deposits in the Qiongdongnan Basin, South China Sea: Relationship with the Pleistocene climate events. Marine Geology, 370: 43–54.CrossRefGoogle Scholar
  43. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P., 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13 (4): 600–612.CrossRefGoogle Scholar
  44. Weimer, P., Slatt, R. M., Bouroullec, R., Fillon, R., Pettingill, H., Pranter, M., and Tari, G., 2006. Introduction to the Petroleum Geology of Deepwater Settings. GeoScienceWorld Books, McLean, Va, 1–846.Google Scholar
  45. Wu, S., Zhang, X., Yang, Z., Wu, T., Gao, J., and Wang, D., 2016. Spatial and temporal evolution of Cenozoic carbonate platforms on the continental margins of the South China Sea: Response to opening of the ocean basin. Interpretation, 4 (3): 1–19.CrossRefGoogle Scholar
  46. Zhen, L., and Chan, A. K., 2001. An artificial intelligent algorithm for tumor detection in screening mammogram. IEEE Transactions on Medical Imaging, 20 (7): 559–567.CrossRefGoogle Scholar
  47. Zhu, W., Wang, Z., Mi, L., Du, X., Xie, X., Lu, Y., Zhang, D., Sun, Z., Liu, X., and Li, Y., 2015. Sequence stratigraphic framework and reef growth unit of well Xike-1 from Xisha Islands, South China Sea. Earth Science–Journal of China University of Geosciences, 40 (4): 677–687 (in Chinese with English abstract).CrossRefGoogle Scholar

Copyright information

© Science Press, Ocean University of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information and Control EngineeringChina University of PetroleumQingdaoChina
  2. 2.Guangzhou Marine Geological SurveyGuangzhouChina
  3. 3.Laboratory of Marine Geophysics & Geo-Resources, Institute of Deep-Sea Science and EngineeringChinese Academy of SciencesSanyaChina

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