Superellipse Fitting for the Recovery and Classification of Mine-Like Shapes in Sidescan Sonar Images

Inst. of Robot., Univ. de Valencia, Valencia
IEEE Journal of Oceanic Engineering (Impact Factor: 1.16). 11/2008; DOI: 10.1109/JOE.2008.2002962
Source: IEEE Xplore

ABSTRACT Mine-like object classification from sidescan sonar images is of great interest for mine counter measure (MCM) operations. Because the shadow cast by an object is often the most distinct feature of a sidescan image, a standard procedure is to perform classification based on features extracted from the shadow. The classification can then be performed by extracting features from the shadow and comparing this to training data to determine the object. In this paper, a superellipse fitting approach to classifying mine-like objects in sidescan sonar images is presented. Superellipses provide a compact and efficient way of representing different mine-like shapes. Through variation of a simple parameter of the superellipse function different shapes such as ellipses, rhomboids, and rectangles can be easily generated. This paper proposes a classification of the shape based directly on a parameter of the superellipse known as the squareness parameter. The first step in this procedure extracts the contour of the shadow given by an unsupervised Markovian segmentation algorithm. Afterwards, a superellipse is fitted by minimizing the Euclidean distance between points on the shadow contour and the superellipse. As the term being minimized is nonlinear, a closed-form solution is not available. Hence, the parameters of the superellipse are estimated by the Nelder-Mead simplex technique. The method was then applied to sidescan data to assess its ability to recover and classify objects. This resulted in a recovery rate of 70% (34 of the 48 mine-like objects) and a classification rate of better than 80% (39 of the 48 mine-like objects).

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    Sonar Systems, 09/2011; , ISBN: 978-953-307-345-3
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    ABSTRACT: Target recognition in sonar imagery has long been an active research area in the maritime domain, especially in the mine-counter measure context. Recently it has received even more attention as new sensors with increased resolution have been developed; new threats to critical maritime assets and a new paradigm for target recognition based on autonomous platforms have emerged. With the recent introduction of Synthetic Aperture Sonar systems and high-frequency sonars, sonar resolution has dramatically increased and noise levels decreased. Sonar images are distance images but at high resolution they tend to appear visually as optical images. Traditionally algorithms have been developed specifically for imaging sonars because of their limited resolution and high noise levels. With high-resolution sonars, algorithms developed in the image processing field for natural images become applicable. However, the lack of large datasets has hampered the development of such algorithms. Here we present a fast and realistic sonar simulator enabling development and evaluation of such algorithms. We develop a classifier and then analyse its performances using our simulated synthetic sonar images. Finally, we discuss sensor resolution requirements to achieve effective classification of various targets and demonstrate that with high resolution sonars target highlight analysis is the key for target recognition.
    Journal on Advances in Signal Processing 01/2010; · 0.81 Impact Factor
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    ABSTRACT: In this paper the problem of contour extraction in sonar images is addressed. We talk in this context about naval mines placed on the seafloor which are still a vast restraint in civil and military shipping. This potential risk is typically encountered by advanced sonar signal processing techniques and a huge amount of human interactions. To reduce at least the human interactions an automatic procedure is desired. Therefore we introduce a novel automatic target extraction algorithm based on active contours employing a specific shadow locating energy motivated by our experiments. Additionally we use a K-means based thresholding process and a Kolmogorov Smirnov (KS) test for improving the initial guess and therefore optimizing the overall performance.

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