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Hierarchical coastline detection in SAR images based on spectral-textural features and global–local information

Hierarchical coastline detection in SAR images based on spectral-textural features and global–local information

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© The Institution of Engineering and Technology
Received 06/02/2019, Accepted 16/08/2019, Revised 15/07/2019, Published 21/08/2019

This study presents a novel approach to detect the coastline from single-polarisation synthetic aperture radar (SAR) images. The proposed method encompasses land/sea segmentation, coastline detection, and refinement. A novel spectral–textural segmentation framework (STSF) is proposed by using the spectral–textural features extracted from the input image patches. The STSF distinguishes various coastal/sea types and is robust to noise. Also, a hierarchical region-based level set method (LSM) is proposed to detect the coastline, accurately. The first LSM step applies global information for evolution. The LSM initialisation is performed using the obtained rough segmentation, which is very practical as the final LSM evolution depends on the initial value, particularly on complex SAR images. The global region-based LSM (GRB-LSM) step modifies the previous segmentation and approaches to the coastline. To improve accuracy, a local region-based LSM (LRB-LSM) is proposed. Therefore, in the second LSM step, the LRB-LSM applies to the results of GRB-LSM step. The LRB-LSM improves the accuracy of the detected coastline while ensuring its smoothness. To verify the performance of the proposed method, several high-resolution SAR images from different microwave bands and various coastal environments are used. The performance of the proposed method is confirmed by the given experiments.

Inspec keywords: geophysical image processing; image segmentation; feature extraction; synthetic aperture radar; radar imaging; image texture

Other keywords: rough segmentation; global region-based LSM step; LRB-LSM; input image patches; global–local information; local region-based LSM; GRB-LSM step; novel spectral–textural segmentation framework; hierarchical region-based level set method; final LSM evolution; hierarchical coastline detection; previous segmentation; complex SAR images; STSF; detected coastline; spectral–textural features; LSM initialisation; high-resolution SAR images; global information; single-polarisation synthetic aperture radar images

Subjects: Optical, image and video signal processing; Radar equipment, systems and applications; Computer vision and image processing techniques; Other topics in statistics; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research

References

    1. 1)
      • 1. Paravolidakis, V., Ragia, L., Moirogiorgou, K., et al: ‘Automatic coastline extraction using edge detection and optimization procedures’, Geosciences (Basel), 2018, 8, (11), p. 407.
    2. 2)
      • 2. Addo, K.A., Walkden, M., Mills, J.P.T.: ‘Detection, measurement and prediction of shoreline recession in Accra, Ghana’, ISPRS J. Photogramm. Remote Sens., 2008, 63, (5), pp. 543–558.
    3. 3)
      • 3. Sousa, W.R.D., Souto, M.V., Matos, S.S., et al: ‘Creation of a coastal evolution prognostic model using shoreline historical data and techniques of digital image processing in a GIS environment for generating future scenarios’, Int. J. Remote Sens., 2018, 39, (13), pp. 4416–4430.
    4. 4)
      • 4. Sekovski, I., Stecchi, F., Mancini, F., et al: ‘Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery’, Int. J. Remote Sens., 2014, 35, (10), pp. 3556–3578.
    5. 5)
      • 5. Qiao, G., Mi, H., Wang, W., et al: ‘55-Year (1960–2015) spatiotemporal shoreline change analysis using historical DISP and LandSat time series data in shanghai’, Int. J. Appl. Earth Obs. Geoinformation, 2018, 68, pp. 238–251.
    6. 6)
      • 6. Henry, J.B., Chastanet, P., Fellah, K., et al: ‘Envisat multi-polarized ASAR data for flood mapping’, Int. J. Remote Sens., 2006, 27, (10), pp. 1921–1929.
    7. 7)
      • 7. Modava, M., Akbarizadeh, G.: ‘A level set based method for coastline detection of SAR images’. Third Int. Conf. Pattern Recognition and Image Analysis (IPRIA), Shahrekord, Iran, April 2017, pp. 253–257.
    8. 8)
      • 8. Yu, Y., Acton, S.: ‘Automated delineation of coastline from polarimetric SAR imagery’, Int. J. Remote Sens., 2004, 25, (17), pp. 3423–3438.
    9. 9)
      • 9. Liu, Y., Huang, H., Qiu, Z., et al: ‘Detecting coastline change from satellite images based on beach slope estimation in a tidal flat’, Int. J. Appl. Earth Obs. Geoinformation, 2013, 23, pp. 165–176.
    10. 10)
      • 10. Liu, C., Xiao, Y., Yang, J.: ‘A coastline detection method in polarimetric SAR images mixing the region-based and edge-based active contour models’, IEEE Trans. Geosci. Remote Sens., 2017, 55, (7), pp. 3735–3747.
    11. 11)
      • 11. An, M., Sun, Q., Hu, J., et al: ‘Coastline detection with Gaofen-3 SAR images using an improved FCM method’, Sensors, 2018, 18, (6), p. 1898.
    12. 12)
      • 12. Modava, M., Akbarizadeh, G.: ‘Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method’, Int. J. Remote Sens., 2017, 38, (2), pp. 355–370.
    13. 13)
      • 13. Liu, C., Yang, J., Yin, J., et al: ‘Coastline detection in SAR images using a hierarchical level set segmentation’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, 9, (11), pp. 4908–4920.
    14. 14)
      • 14. Modava, M., Akbarizadeh, G., Soroosh, M.: ‘Integration of spectral histogram and level set for coastline detection in SAR images’, IEEE Trans. Aerosp. Electron. Syst., 2018, 55, (2), pp. 810–819.
    15. 15)
      • 15. Jana, A., Maiti, S., Biswas, A.: ‘Analysis of short-term shoreline oscillations along Medinipur–Balasore coast, Bay of Bengal, India: a study based on geospatial technology’, Model. Earth Syst. Environ., 2016, 2, (2), p. 64.
    16. 16)
      • 16. Kankara, R., Selvan, S.C., Markose, V.J., et al: ‘Estimation of long and short term shoreline changes along Andhra Pradesh coast using remote sensing and GIS techniques’, Procedia Eng., 2015, 116, pp. 855–862.
    17. 17)
      • 17. Xu, N.: ‘Detecting coastline change with all available LandSat data over 1986–2015: a case study for the state of Texas, USA’, Atmosphere (Basel), 2018, 9, (3), p. 107.
    18. 18)
      • 18. Yu, K., Hu, C., Muller-Karger, F.E., et al: ‘Shoreline changes in west-central Florida between 1987 and 2008 from LandSat observations’, Int. J. Remote Sens., 2011, 32, (23), pp. 8299–8313.
    19. 19)
      • 19. Gens, R.: ‘Remote sensing of coastlines: detection, extraction and monitoring’, Int. J. Remote Sens., 2010, 31, (7), pp. 1819–1836.
    20. 20)
      • 20. Ranjani, J.J., Thiruvengadam, S.: ‘Fast threshold selection algorithm for segmentation of synthetic aperture radar images’, IET Radar Sonar Navig., 2012, 6, (8), pp. 788–795.
    21. 21)
      • 21. Akbarizadeh, G.: ‘A new statistical-based kurtosis wavelet energy feature for texture recognition of SAR images’, IEEE Trans. Geosci. Remote Sens., 2012, 50, (11), pp. 4358–4368.
    22. 22)
      • 22. Braga, F., Tosi, L., Prati, C., et al: ‘Shoreline detection: capability of COSMO-SkyMed and high-resolution multispectral images’, Eur. J. Remote Sens., 2013, 46, (1), pp. 837–853.
    23. 23)
      • 23. Liu, X., Jia, H., Cao, L., et al: ‘Superpixel-based coastline extraction in SAR images with speckle noise removal’. 2016 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), Beijing, China, July 2016, pp. 1034–1037.
    24. 24)
      • 24. Lee, J.-S., Jurkevich, I.: ‘Coastline detection and tracing in SAR images’, IEEE Trans. Geosci. Remote Sens., 1990, 28, (4), pp. 662–668.
    25. 25)
      • 25. Mason, D.C., Davenport, I.J.: ‘Accurate and efficient determination of the shoreline in ERS-1 SAR images’, IEEE Trans. Geosci. Remote Sens., 1996, 34, (5), pp. 1243–1253.
    26. 26)
      • 26. Descombes, X., Moctezuma, M., Maître, H., et al: ‘Coastline detection by a Markovian segmentation on SAR images’, Signal Process., 1996, 55, (1), pp. 123–132.
    27. 27)
      • 27. Liu, H., Jezek, K.: ‘Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods’, Int. J. Remote Sens., 2004, 25, (5), pp. 937–958.
    28. 28)
      • 28. Shu, Y., Li, J., Gomes, G.: ‘Shoreline extraction from RADARSAT-2 intensity imagery using a narrow band level set segmentation approach’, Mar. Geod., 2010, 33, (2–3), pp. 187–203.
    29. 29)
      • 29. Al Fugura, A., Billa, L., Pradhan, B.: ‘Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image’, Estuar. Coast. Shelf Sci., 2011, 95, (4), pp. 395–400.
    30. 30)
      • 30. Sheng, G., Yang, W., Deng, X., et al: ‘Coastline detection in synthetic aperture radar (SAR) images by integrating watershed transformation and controllable gradient vector flow (GVF) snake model’, IEEE J. Ocean. Eng., 2012, 37, (3), pp. 375–383.
    31. 31)
      • 31. Buono, A., Nunziata, F., Mascolo, L., et al: ‘A multipolarization analysis of coastline extraction using X-band COSMO-SkyMed SAR data’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (7), pp. 2811–2820.
    32. 32)
      • 32. Ding, X., Nunziata, F., Li, X., et al: ‘Performance analysis and validation of waterline extraction approaches using single- and dual-polarimetric SAR data’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2015, 8, (3), pp. 1019–1027.
    33. 33)
      • 33. Nunziata, F., Buono, A., Migliaccio, M., et al: ‘Dual-polarimetric C- and X-band SAR data for coastline extraction’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, 9, (11), pp. 4921–4928.
    34. 34)
      • 34. Fuse, T., Ohkura, T.: ‘Development of shoreline extraction method based on spatial pattern analysis of satellite SAR images’, Remote Sens., 2018, 10, (9), p. 1361.
    35. 35)
      • 35. Yuan, J., Wang, D., Cheriyadat, A.M.: ‘Factorization-based texture segmentation’, IEEE Trans. Image Process., 2015, 24, (11), pp. 3488–3497.
    36. 36)
      • 36. Yuan, J., Wang, D., Li, R.: ‘Image segmentation using local spectral histograms and linear regression’, Pattern Recognit. Lett., 2012, 33, (5), pp. 615–622.
    37. 37)
      • 37. Paragios, N., Deriche, R.: ‘Geodesic active contours and level sets for the detection and tracking of moving objects’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (3), pp. 266–280.
    38. 38)
      • 38. Li, C., Xu, C., Gui, C., et al: ‘Level set evolution without re-initialization: a new variational formulation’. IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA, June 2005, pp. 1–7.
    39. 39)
      • 39. Lie, J., Lysaker, M., Tai, X.-C.: ‘A binary level set model and some applications to Mumford–Shah image segmentation’, IEEE Trans. Image Process., 2006, 15, (5), pp. 1171–1181.
    40. 40)
      • 40. Li, C., Kao, C.-Y., Gore, J.C., et al: ‘Minimization of region-scalable fitting energy for image segmentation’, IEEE Trans. Image Process., 2008, 17, (10), p. 1940.
    41. 41)
      • 41. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266–277.
    42. 42)
      • 42. Zhang, K., Zhang, L., Song, H., et al: ‘Active contours with selective local or global segmentation: a new formulation and level set method’, Image Vis. Comput., 2010, 28, (4), pp. 668–676.
    43. 43)
      • 43. Zhang, K., Song, H., Zhang, L.: ‘Active contours driven by local image fitting energy’, Pattern Recognit., 2010, 43, (4), pp. 1199–1206.
    44. 44)
      • 44. Lankton, S., Tannenbaum, A.: ‘Localizing region-based active contours’, IEEE Trans. Image Process., 2008, 17, (11), pp. 2029–2039.
    45. 45)
      • 45. Reis, H.C., Bayram, B., Bozkurt, S., et al: ‘An extended approach of particle swarm optimization for shoreline extraction from RASAT imagery’, J. Indian Soc. Remote Sens., 2018, 46, (8), pp. 1223–1232.
    46. 46)
      • 46. Wang, X., Liu, Y., Ling, F., et al: ‘Fine spatial resolution coastline extraction from LandSat-8 OLI imagery by integrating downscaling and pansharpening approaches’, Remote Sens. Lett., 2018, 9, (4), pp. 314–323.
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