Abstract
For high-resolution side scan sonar images, accurate and fast segmentation of sonar images is crucial for underwater target detection and recognition. However, due to the characteristics of low signal-to-noise ratio (SNR) and complex environmental noise of sonar, the existing methods with high accuracy and good robustness are mostly iterative methods with high complexity and poor real-time performance. For this purpose, a region growing based segmentation using the likelihood ratio testing method (RGLT) is proposed. This method obtains the seed points in the highlight and the shadow regions by likelihood ratio testing based on the statistical probability distribution and then grows them according to the similarity criterion. The growth avoids the processing of the seabed reverberation regions, which account for the largest proportion of sonar images, thus greatly reducing segmentation time and improving segmentation accuracy. In addition, a pre-processing filtering method called standard deviation filtering (STDF) is proposed to improve the SNR and remove the speckle noise. Experiments were conducted on three sonar databases, which showed that RGLT has significantly improved quantitative metrics such as accuracy, speed, and segmentation visual effects. The average accuracy and running times of the proposed segmentation method for 100 × 400 images are separately 95.90% and 0.44 s.
Highlights
The results demonstrate that the proposed standard deviation filtering (STDF) has higher signal-to-noise ratio (SNR) and contrast than other methods
The segmentation accuracy ρ is used for the overall evaluation of the whole segmentation results, which is calculated as follows
A filter referring to as STDF is proposed in this paper to enhance the SNR and contrast of the sonar image
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Sidescan sonar (SSS), which can provide high-resolution images of the seabed, is one of the most common sensors for various underwater applications, such as topography measurement [1], search for sunken vessels and submerged settlements [2], underwater mine detection [3], fish stocks detection, cable or pipeline detection [4,5,6], and offshore oil prospecting [7]. Accurate and efficient segmentation of SSS images is essential for underwater objects detection. Because segmenting sonar images into highlight areas with objects, regions of shadow, and seafloor reverberation is an effective method to obtain the region of interest (ROI), this is usually an important step before object classification
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