1 research outputs found
Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation
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