Simple Methodology for Deriving Continuous Shorelines from Imagery: Application to Rivers
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 139, Issue 5
Abstract
A methodology is developed to extract and process shoreline data, the interface between land and water, identified from imagery. Initially, image pixels containing water (water points) and pixel locations of the land/water interface (edge points) are extracted from an image using either a supervised, threshold approach or a newly developed, automated texture-based analysis. Both are described and demonstrated. Subsequently applied is a procedure for processing these edge and water point locations to obtain oriented and ordered shoreline coordinates. The described methodology has several advantages: (1) shoreline processing is independent of imagery source and resolution, that is, specification of search directions based on image resolution or desired shoreline resolution is unnecessary and (2) a need for additional postprocessing of remote-sensed data or extracted-edge data are obviated, that is, edge data need not be of high quality or vectorized. Details of the entire methodology, including algorithms for water and edge point extraction from imagery and specifics of the processing applied to obtain an ordered, oriented shoreline, are presented. Execution of the complete shoreline extraction algorithm is demonstrated independently by application to sections of the East Pearl River, Mississippi, and the Kootenai River, Idaho. A quantitative performance measure of the ability of the algorithm to produce realistic-ordered, oriented shoreline coordinates from a set of edge and water point data extracted from imagery results in RMS errors of 1 m or less or two times the ground sample distance of the imagery.
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Acknowledgments
The authors thank A. Weidemann for providing the remotely sensed data and C. Daniels and P. Flynn for processing the remotely sensed data for the East Pearl River. The authors gratefully acknowledge the contributions of four anonymous reviewers. Funding for this research is provided by the Office of Naval Research through the NRL 6.2 project, Demonstration and Assessment of a Self-Organizing Adaptive Underwater Network. This work is Naval Research Laboratory contribution number NRL/JA/7320-11-797.
References
Awate, S., Tasdizen, T., and Whitaker, R. (2006). “Unsupervised texture segmentation with nonparametric neighborhood statistics.” Proc., European Conf. Comput. Vision, Springer-Verlag, Berlin, 494–507.
Barton, G. J., McDonald, R. R., Nelson, J. M., and Dinehart, R. L. (2005). “Simulation of flow and sediment mobility using a multidimensional flow model for the white sturgeon critical habitat reach, Kootenai River near Bonners Ferry, Idaho.” Scientific Investigations Rep. 2005-5230, Dept. of the Interior, U.S. Geological Survey, Washington, DC.
Blain, C. A., Linzell, R., Massey, T. C., and Estrade, B. D. (2007). “MESHGUI: A mesh generation and editing toolset for the ADCIRC model.” NRL Memorandum Rep. NRL/MR/7320-2007-9083, Naval Research Laboratory, Washington, DC.
Damron, J. J. (2000). “Generating a coastal boundary and merging bathymetry with DTED level 1 using ArcInfo: A modeling and simulation application.” Rep. USACE ERDC/TEC TN-00-1, Dept. of the Army, Engineering Research and Development Center, Vicksburg, MS.
ENVI 4.8 [Computer software]. Boulder, CO, Exelis Visual Information Solutions.
Erkan, A., Camp-Valls, G., and Altun, Y. (2010). “Semi-supervised remote sensing image classification via maximum entropy.” Proc., 2010 IEEE Int. Workshop on Machine Learning for Signal Processing, IEEE, New York, 313–318.
Fleming, J. N. (2005). “Design of a semi-automatic algorithm for shoreline extraction using synthetic aperature radar (SAR).” Geodesy and Geomatics Engineering, Technical Rep. No. 231, Univ. of New Brunswick, Fredericton, NB, Canada.
Gorman, G. J., Piggott, M. J., and Pain, C. C. (2007). “Shoreline approximation for unstructured mesh generation.” Comput. Geosci., 33(5), 666–667.
Gorman, G. J., Piggott, M. J., Wells, M. R., Pain, C. C., and Allison, P. A. (2008). “A systematic approach to unstructured mesh generation for ocean modeling using GMT and Terreno.” Comput. Geosci., 34(12), 1721–1731.
Kolingerova, I., and Zalik, B. (2006). “Reconstructing boundaries within a given set of points, using Delaunay triangulation.” Comput. Geosci., 32(9), 1310–1319.
Lee, J.-S., and Jurkevich, I. (1990). “Coastline detection and tracing in SAR images.” IEEE Trans. Geosci. Rem. Sens., 28(4), 662–668.
Li, R., Di, K., and Ma, R. (2004). “A comparative study on shoreline mapping techniques.” GIS for coastal zone management, CRC Press, Boca Raton, FL.
Liu, H., and Jezek, K. C. (2004a). “A complete high-resolution coastline of Antarctica extracted from orthorectified Radarsat SAR imagery.” Photogramm. Eng. Remote Sensing, 70(5), 605–616.
Liu, H., and Jezek, K. C. (2004b). “Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods.” Int. J. Remote Sens., 25(5), 937–958.
Maras, H. H. (2008). “Automatic extraction of coastlines from SAR images.” Sea Technol., 49(7), 33–36.
Mason, D. C., and Davenport, I. J. (1996). “Accurate and efficient determination of the shoreline in ERS-1 SAR images.” IEEE Trans. Geosci. Rem. Sens., 34(5), 1243–1253.
McKay, P., Blain, C. A., and Linzell, R. S. (2011). “Automated identification of rivers and shorelines in aerial imagery using image texture.” Proc., SPIE, 8030, 80300G.
Parrish, C. E., Sault, M., White, S., and Sellars, J. (2005). “Empirical analysis of aerial camera filters for shoreline mapping.” Proc., American Soc. Photogrammetry and Remote Sensing (ASPRS) (CD-ROM), American Society for Photogrammetry and Remote Sensing, Baltimore.
Pearl River Basin Development District. (2012). “Topography and history.” Pearl River Basin Development District, 〈http://www.pearlriverbasin.com/topography_and_history.php〉 (Jan. 20, 2012).
Pharwaha, A., and Singh, B. (2009). “Shannon and non-Shannon entropy for statistical texture extraction in digitized mammograms.” Proc., World Congress on Engineering and Computer Science, Newswood Limited, International Association of Engineers, Hong Kong, 1286–1291.
Pratt, W. (1991). Digital signal processing, Wiley, New York.
Sera, J. (1983). Image analysis and mathematical morphology, Academic Press, Maryland Heights, MO.
Shannon, C. (1948). “A mathematical theory of communication.” Bell Syst. Tech. J., 27(4), 379–423.
Singh, B., and Singh, A. (2008). “Edge detection in gray level images based on Shannon entropy.” J. Comput. Sci., 4(3), 186–191.
Soluri, E. A., and Woodson, V. A. (1990). “World vector shoreline.” Int. Hydrogr. Rev., 67(1), 27–36.
Tuceryan, M. (1994). “Moment based texture segmentation.” Pattern Recognit. Lett., 15(7), 659–668.
Tuceryan, M., and Jain, A. (1990). “Texture segmentation using Vornoi polygons.” IEEE Trans. Pattern Anal. Mach. Intell., 12(2), 211–216.
Tuceryan, M., and Jain, A. (1998). “Texture analysis.” Chapter 2.1, Handbook of pattern recognition and computer vision, World Scientific, Singapore, 207–248.
Viyas, V., and Rege, P. (2006). “Automated texture analysis with Gabor filters.” Graphics Visual. Image Process., 6(1), 35–41.
Wessel, P., and Smith, W. H. F. (1996). “A global self-consistent, hierarchical, high resolution shoreline database.” J. Geophys. Res., 101(B4), 8741–8743.
Xu, X., and Miller, E. (2002). “Entropy optimized contrast stretch to enhance remote sensed imagery.” Proc., 16th Int. Conf. Pattern Recognition, IEEE, Los Alamitos, CA, 915–918.
Yan, J., Chen, S., and Wu, Y. (2010). “Remote sensing image classification based on fuzzy entropy triple-I algorithm.” Quantitative logic and soft computing 2010, Cao, B.-Y. et al., eds., Vol. 2, Springer-Verlag, Berlin, 473–482.
Yu, Y., and Acton, S. T. (2004). “Automated delineation of coastline from polarimetric SAR imagery.” Int. J. Remote Sens., 25(17), 3423–3438.
Zhang, Y. (2000). “A method for continuous extraction of multispectrally classified urban rivers.” J. Photogramm. Eng. Remote Sens., 66(8), 991–999.
Zhou, G., and Li, R. (2000). “Accuracy evaluation of ground points from IKONOS high-resolution satellite imagery.” J. Photogramm. Eng. Remote Sens., 66(9), 1103–1112.
Zundel, A. K. (2000). Surface-water modeling system (SMS) reference manual, Brigham Young Univ., Environmental Modeling Research Laboratory, Provo, UT.
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Published In
Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 139 • Issue 5 • September 2013
Pages: 365 - 382
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Nov 30, 2011
Accepted: Nov 15, 2012
Published online: Nov 19, 2012
Published in print: Sep 1, 2013
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