Enhanced Fuzzy-Based Local Information Algorithm for Sonar Image Segmentation

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 de...

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Bibliographic Details
Published in:IEEE transactions on image processing 2020-01, Vol.29, p.445-460
Main Authors: Abu, Avi, Diamant, Roee
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Fuzzy clustering
Historic sites
image de-noising
Image denoising
Image detection
Image enhancement
Image segmentation
Inhomogeneity
intensity inhomogeneity
kernel-induced distance
Noise reduction
Nonhomogeneous media
Ocean floor
Robustness
Sonar
sonar image segmentation
speckle noise
Synthetic aperture sonar
Synthetic apertures
Undersea
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2930148