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Implementation of Digital Image Processing for Coastline Extraction from Synthetic Aperture Radar Imagery  

Lee, Dong-Cheon (Department of Geoinformation Engineering, Sejong University)
Seo, Su-Young (Inha University)
Lee, Im-Pyeong (Department of Geoinformatics, The University of Seoul)
Kwon, Jay-Hyoun (Department of Geoinformatics, The University of Seoul)
Tuell, Grady H. (Optech International)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.25, no.6_1, 2007 , pp. 517-528 More about this Journal
Abstract
Extraction of the coastal boundary is important because the boundary serves as a reference in the demarcation of maritime zones such as territorial sea, contiguous zone, and exclusive economic zone. Accurate nautical charts also depend on well established, accurate, consistent, and current coastline delineation. However, to identify the precise location of the coastal boundary is a difficult task due to tidal and wave motions. This paper presents an efficient way to extract coastlines by applying digital image processing techniques to Synthetic Aperture Radar (SAR) imagery. Over the past few years, satellite-based SAR and high resolution airborne SAR images have become available, and SAR has been evaluated as a new mapping technology. Using remotely sensed data gives benefits in several aspects, especially SAR is largely unaffected by weather constraints, is operational at night time over a large area, and provides high contrast between water and land areas. Various image processing techniques including region growing, texture-based image segmentation, local entropy method, and refinement with image pyramid were implemented to extract the coastline in this study. Finally, the results were compared with existing coastline data derived from aerial photographs.
Keywords
SAR image; Coastline; Digital image processing; Accuracy assessment;
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1 Bovik, A., Clark, M. and Geisler, W. (1990), Multichannel Texture Analysis Using Localized Spatial Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, pp. 55-73   DOI   ScienceOn
2 Daugman, J. (1985), Uncertainty Relation for resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. Journal of Optical Society of America, 7, pp. 1160-1169
3 Maling, D. (1989), Measurements from Maps: Principles and Methods of Cartometry, Pergamon Press, Oxford, 577 p.
4 Pratt, W. (2001), Digital Image Processing, 3'd ed. John Wiley & Sons, Inc., New York, 735 p
5 Tuell, G. (1998), The Use of High Resolution Airborne Synthetic Aperture Radar (SAR) for Shoreline Mapping. International Archives of Photogrammetry & Remote Sensing, 32-3/2, pp. 592-611
6 Jensen, J. (1996), Introductory Digital Image Processing: A Remote Sensing Perspective, 2nd ed. Prentice-Hall, Inc., Upper Saddle River, 318 p
7 Schowengertdt, R. (1997), Remote Sensing: Models and Methods for Image Processing, 2nd ed. Academic Press, San Diego, 522p.
8 Lee, D.C. and Schenk, T. (1998), An Adaptive Approach for Extracting Texture Information and Segmentation. International Archives of Photogrammetry and Remote Sensing, 32-3/1, pp. 250-255
9 Jahne, B. and HauBecker, H. (2000), Computer Vision and Applications. Academic Press, San Diego, 679 p.
10 Lui, H. and Kenneth, J. (2004), A Complete High-Resolution Coastline of Antarctica Extracted from Orthorectified Radarsat SAR Imagery. Photogrammetric Engineering & Remote Sensing, 70, pp. 605-615   DOI
11 NOAA's Coastal Mapping Web-site, http://www.ngs.noaa.gov/ RSD/coastal/index.html
12 Russ, J. (2002), The Image Processing Handbook, 4th ed. CRC Press, Raleigh, 732 p.
13 Schenk, T. (1999), Digital Photogrammetry, Vol. 1, Terra-Science, Laurelville, 428p.
14 Turner, M. (1986), Texture Discrimination by Gabor Function, Biological Cybernetics, 55, pp. 71-82
15 Dunn, D. and Higgins, W. (1995), Optimal Gabor Filters for Texture Segmentation. IEEE Transactions on Image Processing, 4, pp. 947-964   DOI   ScienceOn
16 Schalkoff, R. (1989), Digital Image Processing and Computer Vision, John Wiley & Sons, New York, 489 p.