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Proceedings Paper

Detection of seagrass scars using sparse coding and morphological filter
Author(s): Ender Oguslu; Sertan Erkanli; Victoria J. Hill; W. Paul Bissett; Richard C. Zimmerman; Jiang Li
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Paper Abstract

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve <90% accuracy on the detection of seagrass scars.

Paper Details

Date Published: 14 October 2014
PDF: 9 pages
Proc. SPIE 9240, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2014, 92400G (14 October 2014); doi: 10.1117/12.2069325
Show Author Affiliations
Ender Oguslu, Old Dominion Univ. (United States)
Turkish Air Force NCO Vocational College (Turkey)
Sertan Erkanli, Turkish Air Force (Turkey)
Victoria J. Hill, Old Dominion Univ. (United States)
W. Paul Bissett, Florida Environmental Research Institute (United States)
Richard C. Zimmerman, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 9240:
Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2014
Charles R. Bostater Jr.; Stelios P. Mertikas; Xavier Neyt, Editor(s)

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