# Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation

arXiv: https://arxiv.org/abs/1709.10180 · HTML: https://mlsanity.com/paper/1709.10180/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation](https://mlsanity.com/paper/2110.07433/) (2021)
- [Iterative, Deep Synthetic Aperture Sonar Image Segmentation](https://mlsanity.com/paper/2203.15082/) (2022)
- [Histogram Layers for Synthetic Aperture Sonar Imagery](https://mlsanity.com/paper/2209.03878/) (2022)
- [The Weakly-Labeled Rand Index](https://mlsanity.com/paper/2103.04872/) (2021)
- [Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentation](https://mlsanity.com/paper/2107.14563/) (2021)

## Abstract (arXiv)

Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor. When viewing this imagery, a human observer can often easily identify various sea-floor textures such as sand ripple, hard-packed sand, sea grass and rock. In this paper, we present the Possibilistic Fuzzy Local Information C-Means (PFLICM) approach to segment SAS imagery into sea-floor regions that exhibit these various natural textures. The proposed PFLICM method incorporates fuzzy and possibilistic clustering methods and leverages (local) spatial information to perform soft segmentation. Results are shown on several SAS scenes and compared to alternative segmentation approaches.