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.
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© 2013 American Society of Civil Engineers.
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Received: Nov 30, 2011
Accepted: Nov 15, 2012
Published online: Nov 19, 2012
Published in print: Sep 1, 2013
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