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

TL;DR

This paper introduces an automated feature selection method integrated with PFLICM to improve seafloor segmentation in SAS imagery, enhancing accuracy through optimized feature subsets.

Contribution

It presents a novel automated feature selection approach combined with PFLICM for improved seafloor segmentation in SAS images.

Findings

Automated feature selection improves segmentation accuracy.

Selected features meet a specific clustering validity threshold.

Enhanced segmentation results compared to non-automated methods.

Abstract

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.