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Approach for rapid segmentation of coastline based on the C-V model using the exponential sequence of multi-scale SAR images

  • role： First author第一作者
  • Affiliation：

    College of Geomatics, Shandong University of Science and Technology, Qingdao 266510, China

    State Key Laboratory, Chinese Academy of Surveying & Mapping Geo Spatial Information Engineering, State Key Laboratory of Surveying and Mapping Geographic Information Bureau (National Geographic Situation Monitoring Research Center), Beijing 100830, China

  • Email：rshujj@163.com
  • Introduction：胡娇静(1993—　)，女，硕士研究生，研究方向为遥感图像目标识别、微波遥感等。E-mail：rshujj@163.com
Jiaojing HU12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Surveying Mapping Product Quality Inspection and Testing Center, Beijing 100830, China

  • Introduction：张继贤(1965— )，男，研究员，研究方向为摄影测量与遥感、地理信息系统、资源与环境遥感监测。E-mail：zhangjx@casm.ac.cn
Jixian ZHANG3*,  
  • Affiliation：

    College of Geomatics, Shandong University of Science and Technology, Qingdao 266510, China

Junyi XU1,  
  • Affiliation：

    State Key Laboratory, Chinese Academy of Surveying & Mapping Geo Spatial Information Engineering, State Key Laboratory of Surveying and Mapping Geographic Information Bureau (National Geographic Situation Monitoring Research Center), Beijing 100830, China

Zheng ZHAO2

resumen

The coastline, which defines the boundary between the sea and land, is the benchmark for the division of national territory and the exclusive economic zones of oceans, and it is crucial to the maintenance of the rights to oceans. Extracting coastline information rapidly and accurately has become a popular issue in ocean research. However, detecting the coastline from SAR images is challenging because the information from land and sea can be mixed in a complex landform, and the reflection information from the sea area is changeable. The C-V model possesses strong detection sensitivity and anti-noise capability for coastline detection. However, the method involves complex iterations and large amounts of calculation. The speed of coastline detection is low for large-sized SAR images. Reducing the number of iterations and improving testing efficiency are the focus of this study, and the accuracy of the results are guaranteed under this premise. Given that the existing binary C-V model for coastline segmentation has low convergence speed and is highly sensitive to initial conditions, a novel approach using the exponential sequence of SAR images is proposed to improve the accuracy and efficiency of automatic coastline segmentation of SAR images. In this study, the image sequence generated by the traditional method is changed to an exponential sequence based on any bottom number greater than 1. The coastline is automatically detected based on predefined features from the generated image sequence. The main research techniques adopted are as follows. (1) The exponential multi-scale technology of the traditional C-V model that reduces image size and obtains a series of images under different spatial resolutions is introduced. (2) A low-pass filter is used to polish a small-scale image sequence, which is easy to form and has a relatively smooth boundary. (3) The exponential image sequence with different scales and polished degrees splits the coastline based on the C-V model of the level set one by one. In the division of the coastline, the high-spatial-resolution image inherits the boundary extracted by using low-spatial-resolution images in the high level and refines the coastline further through the C-V model. Coastline detection accuracy is ensured, and experimental results show that the computation effort of each iteration in the presented approach is less than that in the segmentation procedure based on the binary C-V model. The amount of iteration frequency is also reduced. The multi-scale method achieves image conversion from a low scale to a high scale. The low-pass filter removes small and pointless feature information, which improves the level set of the C-V model to achieve a simple iteration, significantly reduces time, and refines the boundary. The entire process ensures efficiency, high precision of edge extraction, and fast running time. Compared with the traditional binary C-V model, the presented approach is more efficient and effective in terms of computation effort and detection accuracy. Experimental results show that the presented method accelerates the acquisition of initial level set information and shortens the time of coastline extraction. The method also removes the non-coastline body and improves the detection precision of the main coastline body, thus ensuring a robust coastline detection process.

palabra clave

SAR;exponential image sequence;coastline segmentation;multi-scale;C-V model

References

  1. 1.
    Baselice F, Ferraioli G and Pascazio V. 2012. Coastal line extraction from SAR multi-channel images//Proceedings of 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing. Naples: IEEE, 2012: 322–325 [DOI: 10.1109/TyWRRS.2012.6381149]
  2. 2.
    Boak E H and Turner I L. 2005. Shoreline definition and detection: a review. Journal of Coastal Research, 21(4): 688–703
  3. 3.
    Brox T and Weickert J. 2004. Level set based image segmentation with multiple regions//Proceedings of the 26th DAGM Symposium. Tübingen, Germany: Springer: 415–423 [DOI: 10.1007/978-3-540-28649-3_51]
  4. 4.
    Cao G, Mao Z H, Yang X and Xia D S. 2008. Optical aerial image partitioning using level sets based on modified Chan-Vese mode. Pattern Recognition Letters, 29(4): 457–464
  5. 5.
    Chan T F and Vese L A. 2001. Active contours without edges. IEEE Transactions on Image Processing, 10(2): 266–277
  6. 6.
    Chen W W and Chang H K. 2009. Estimation of shoreline position and change from satellite images considering tidal variation. Estuarine, Coastal and Shelf Science, 84(1): 54–60
  7. 7.
    Chen X, Sun J, Yin K Y and Yu J P. 2014. Sea-land segmentation algorithm of SAR image based on Otsu method and statistical characteristic of sea area. Journal of Data Acquisition and Processing, 29(4): 603–608 (
  8. 8.
    Courant R, Friedrichs K and Lewy H. 1967. On the partial difference equations of mathematical physics. IBM Journal of Research and Development, 11(2): 215–234
  9. 9.
    Dellepiane S, De Laurentiis R and Giordano F. 2004. Coastline extraction from SAR images and a method for the evaluation of the coastline precision. Pattern Recognition Letters, 25(13): 1461–1470
  10. 10.
    Deng S P, Zhang J X and Li P X. 2011. Review of edge detection from polarimetric SAR imagery. Computer Engineering and Applications, 47(22): 1–5, 16 (
  11. 11.
    Dragotti P L, Vetterli M and Blu T. 2005. Exact sampling results for signals with finite rate of innovation using Strang-Fix conditions and local kernels//Proceedings of 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, PA, USA: IEEE: 223–236 [DOI: 10.1109/ICASSP.2005.1415988]
  12. 12.
    Feng W Q and Zhang Y J. 2015. Object-oriented change detection for remote sensing images based on multi-scale fusion. Acta Geodaetica et Cartographica Sinica, 44(10): 1142–1151 (
  13. 13.
    Gao S and Bui T D. 2005. Image segmentation and selective smoothing by using Mumford-Shah model. IEEE Transactions on Image Processing, 14(10): 1537–1549
  14. 14.
    Gu D D, Xu X J and Zhang X L. 2013. SAR (Synthetic Aperture Radar) image sea-land segmentation method based on wavelet transform and OTSU threshold. CN, CN102968798A
  15. 15.
    Hai G S, Xu C, Liu J Y, Sun K M and Wen C F. 2012. A novel multi-scale level set method for SAR image segmentation based on a statistical model. International Journal of Remote Sensing, 33(17): 5600–5614
  16. 16.
    Heygster G, Dannenberg J and Notholt J. 2010. Topographic mapping of the german tidal flats analyzing SAR images with the waterline method. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1019–1030
  17. 17.
    Hu J J, Zhao Z, Wang Z Y and Wang S S. 2017. Comparison and effectiveness evaluation for methods of coastline extraction from SAR imagery. Remote Sensing Information, 32(4): 58–63 (
  18. 18.
    Kou G J, Wang Z S and Yao P. 2011. Filtering methods for different characteristic stripe noise in SAR image. Journal of Remote Sensing, 15(1): 1–12 (
  19. 19.
    Lie J, Lysaker M and Tai X C. 2006. A binary level set model and some applications to Mumford-Shah image segmentation. IEEE Transactions on Image Processing, 15(5): 1171–1181
  20. 20.
    Lin Z Z, Lv K, Yang L, Xu W and Zhai R. 2014. A method for coastline detection based on level set. Acta Electronica Sinica, 42(2): 391–397 (
  21. 21.
    Liu P C. 2015. Feature extraction of coastline contour based on level set theory. Remote Sensing for Land and Resources, 27(2): 75–79 (
  22. 22.
    Mason D C and Davenport I J. 1996. Accurate and efficient determination of the shoreline in ERS-1 SAR images. IEEE Transactions on Geoscience and Remote Sensing, 34(5): 1243–1253
  23. 23.
    Ogawa H. 1986. A unified approach to generalized sampling theorems//Proceedings of 1986 IEEE International Conference on Acoustics, Speech, and Signal Processing. Tokyo, Japan: IEEE: 1657–1660 [DOI: 10.1109/ICASSP.1986.1168936]
  24. 24.
    Osher S and Fedkiw R. 2002. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag: 3–124
  25. 25.
    Ouyang Y and Zhong J S. 2004. Coastline detection method in SAR images based on an improved level set algorithm. Remote Sensing Technology and Application, 19(6): 456–460 (
  26. 26.
    Sonka M, Hlavac V and Boyle R. 2008. Image Processing, Analysis, and Machine Vision. 3rd ed. Australia: Thomson Learning: 275–282
  27. 27.
    Tao K and Zhu J J. 2012. A hybrid indicator for determining the best decomposition scale of wavelet Denoising. Acta Geodaetica et Cartographica Sinica, 41(5): 749–755 (
  28. 28.
    Wang D K, Hou Y Q and Peng J Y. 2008. Partial Differential Equation Method for Image Processing. Beijing: Science Press: 1–121
  29. 29.
    Wu S H, Wu Y Q, Zhou J J and Meng T L. 2015. SAR river image segmentation based on reciprocal gray entropy and improved Chan-Vese model. Acta Geodatetica et Cartographica Sinica, 44(11): 1255–1262 (
  30. 30.
    Xie M H, Zhang Y F and Fu K. 2007. Algorithm of detection coastline from SAR images based on seeds growing. Journal of the Graduate School of the Chinese Academy of Science, 24(1): 93–98 (
  31. 31.
    Xu C, Hua F, Sui H G and Chen G. 2014. Automatic water segmentation method in SAR images using Muti-scale Level Set. Geomatics and Information Science of Wuhan University, 39(1): 27–31 (
  32. 32.
    Yu C X, Wang J Y, Xu J, Peng R C, Cheng Y and Wang M. 2014. Advance of coastline extraction technology. Journal of Geomatics Science and Technology, 31(3): 305–309 (
  33. 33.
    Zhang K H, Zhang L, Song H H and Zhou W G. 2010. Active contours with selective local or global segmentation: a new formulation and level set method. Images and Vision Computing, 28(4): 668–676

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