Enhanced Fuzzy-Based Local Information Algorithm for Sonar Image Segmentation

Research output: Contribution to journalArticlepeer-review


The recent boost in undersea operations has led to the development of high-resolution sonar systems mounted on autonomous vehicles. These vehicles are used to scan the seafloor in search of different objects such as sunken ships, archaeological sites, and submerged mines. An important part of the detection operation is the segmentation of sonar images, where the object's highlight and shadow are distinguished from the seabed background. In this work, we focus on the automatic segmentation of sonar images. We present our enhanced fuzzybased with Kernel metric (EnFK) algorithm for the segmentation of sonar images which, in an attempt to improve segmentation accuracy, introduces two new fuzzy terms of local spatial and statistical information. Our algorithm includes a preliminary de-noising algorithm which, together with the original image, feeds into the segmentation procedure to avoid trapping to local minima and to improve convergence. The result is a segmentation procedure that specifically suits the intensity inhomogeneity and the complex seabed texture of sonar images. We tested our approach using simulated images, real sonar images, and sonar images that we created in two different sea experiments, using multibeam sonar and synthetic aperture sonar. The results show accurate segmentation performance that is far beyond the stateof-the-art results.

Original languageEnglish
Article number8779589
Pages (from-to)445-460
Number of pages16
JournalIEEE Transactions on Image Processing
Early online date29 Jul 2019
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.


  • Fuzzy clustering
  • image de-noising
  • intensity inhomogeneity
  • kernel-induced distance
  • sonar image segmentation
  • speckle noise

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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