Unsupervised Local Spatial Mixture Segmentation of Underwater Objects in Sonar Images

Avi Abu, Roee Diamant

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we focus on the segmentation of sonar images to achieve underwater object detection and classification. Our goal is to achieve accurate segmentation of the object's highlight and shadow regions. We target a robust solution that can manage different seabed backgrounds. Segmentation of sonar images is a challenging task. Speckle noise and intensity inhomogeneity may cause false detections and complex seabed textures, such as sand ripples and seagrass, often leading to false segmentation. In this paper, we propose our local spatial mixture (LSM) method for image segmentation of sidescan deployed sonar systems of any type. This new method estimates pixel labels in sonar images by incorporating the possible spatial correlation between neighboring pixels for improved segmentation. LSM modifies the expectation-maximization algorithm by adding an intermediate step (I-step) between the expectation (E-step) and maximization (M-step) steps. To combat intensity inhomogeneity, we employ a new initialization algorithm, one whose thresholds are set automatically to achieve and maintain robustness in various underwater environments. Using multiple evaluation indexes that measure the geometrical features of the segmented objects, we tested LSM using synthetic and real sonar images, one of which is obtained from our own sea experiment. Our results show that LSM achieves improved segmentation performance over the state-of-the-art methods of four different approaches; LSM is also robust to different seabed textures and intensity inhomogeneity. We share the sonar images from our sea experiments.

Original languageEnglish
Article number8444675
Pages (from-to)1179-1197
Number of pages19
JournalIEEE Journal of Oceanic Engineering
Volume44
Issue number4
DOIs
StatePublished - Oct 2019

Bibliographical note

Funding Information:
Manuscript received November 12, 2017; revised March 19, 2018 and June 6, 2018; accepted August 1, 2018. Date of publication August 23, 2018; date of current version October 11, 2019. This work was supported by the NATO Science for Peace and Security Programme under Grant G5293. This paper was presented in part at the 2017 IEEE OCEANS Conference, Aberdeen, Scotland, June 2017. (Corresponding author: Avi Abu.) Associate Editor: J. Cobb.

Publisher Copyright:
© 1976-2012 IEEE.

Keywords

  • Expectation maximization (EM)
  • gamma distribution
  • object detection
  • sand ripples
  • sonar image segmentation
  • speckle noise

ASJC Scopus subject areas

  • Ocean Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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