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

Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation