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Proceedings Paper

A new morphology algorithm for shoreline extraction from DEM data
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Paper Abstract

Digital elevation models (DEMs) are a digital representation of elevations at regularly spaced points. They provide an accurate tool to extract the shoreline profiles. One of the emerging sources of creating them is light detection and ranging (LiDAR) that can capture a highly dense cloud points with high resolution that can reach 15 cm and 100 cm in the vertical and horizontal directions respectively in short periods of time. In this paper we present a multi-step morphological algorithm to extract shorelines locations from the DEM data and a predefined tidal datum. Unlike similar approaches, it utilizes Lowess nonparametric regression to estimate the missing values within the DEM file. Also, it will detect and eliminate the outliers and errors that result from waves, ships, etc by means of anomality test with neighborhood constrains. Because, there might be some significant broken regions such as branches and islands, it utilizes a constrained morphological open and close to reduce these artifacts that can affect the extracted shorelines. In addition, it eliminates docks, bridges and fishing piers along the extracted shorelines by means of Hough transform. Based on a specific tidal datum, the algorithm will segment the DEM data into water and land objects. Without sacrificing the accuracy and the spatial details of the extracted boundaries, the algorithm should smooth and extract the shoreline profiles by tracing the boundary pixels between the land and the water segments. For given tidal values, we qualitatively assess the visual quality of the extracted shorelines by superimposing them on the available aerial photographs.

Paper Details

Date Published: 29 April 2013
PDF: 10 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480D (29 April 2013); doi: 10.1117/12.2015801
Show Author Affiliations
Amr Hussein Yousef, Old Dominion Univ. (United States)
Khan Iftekharuddin, Old Dominion Univ. (United States)
Mohammad Karim, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)

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