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.
Joshua Peeples, Matthew Cook, Daniel Suen, Alina Zare, and James Keller, "Comparison of possibilistic fuzzy local information C-means and possibilistic K-nearest neighbors for synthetic aperture sonar image segmentation," Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 110120T (Presented at SPIE Defense + Commercial Sensing: April 16, 2019; Published: 10 May 2019); https://doi.org/10.1117/12.2519484.
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