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Proceedings Paper

Comparison of possibilistic fuzzy local information C-means and possibilistic K-nearest neighbors for synthetic aperture sonar image segmentation
Author(s): Joshua Peeples; Matthew Cook; Daniel Suen; Alina Zare; James Keller
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Paper Abstract

Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow for the ability to detect novel or outlier environments as well as well known classes. The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been previously applied to segment SAS imagery. Additionally, the Possibilistic K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as landmine detection and hyperspectral imagery. In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN. We include final segmentation results on multiple SAS images and a quantitative assessment of each algorithm.

Paper Details

Date Published: 10 May 2019
PDF: 10 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 110120T (10 May 2019); doi: 10.1117/12.2519484
Show Author Affiliations
Joshua Peeples, Univ. of Florida (United States)
Matthew Cook, Univ. of Florida (United States)
Daniel Suen, Univ. of Florida (United States)
Alina Zare, Univ. of Florida (United States)
James Keller, Univ. of Missouri (United States)

Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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