  The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is
presented as a technique to segment side-look synthetic aperture sonar (SAS)
imagery into distinct regions of the sea-floor. In this work, we investigate
and present the results of an automated feature selection approach for SAS
image segmentation. The chosen features and resulting segmentation from the
image will be assessed based on a select quantitative clustering validity
criterion and the subset of the features that reach a desired threshold will be
used for the segmentation process.
