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 paper, we focus on the automatic segmentation of sonar images. We present our enhanced fuzzy-based 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 were created in two different sea experiments, using multibeam sonar and synthetic aperture sonar. The results show accurate segmentation performance that is far beyond the state-of-the-art results.
Bibliographical noteFunding Information:
Manuscript received August 2, 2018; revised June 13, 2019; accepted July 5, 2019. Date of publication July 29, 2019; date of current version September 23, 2019. This work was supported in part by the NATO Science for Peace and Security Program under Grant G5293. This paper was presented in part at the Oceans Conference, Kobe, Japan, in 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gene Cheung. (Corresponding author: Avi Abu.) The authors are with the Department of Marine Technology, University of Haifa, Haifa 3498838, Israel (e-mail: email@example.com; firstname.lastname@example.org). Digital Object Identifier 10.1109/TIP.2019.2930148
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- Fuzzy clustering
- image de-noising
- intensity inhomogeneity
- kernel-induced distance
- sonar image segmentation
- speckle noise
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design