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Fuzzy Preprocessing and Clustering Analysis Method of Underwater Multiple Targets in Forward Looking Sonar Image for AUV Tracking

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Abstract

Marine sonar image noise absorption under water can result in poor image quality, low image resolution and blurred target contour. This paper proposes a systematic framework including underwater image fuzzy preprocessing method, and clustering based on the principal component analysis (PCA) of underwater targets. Underwater images were denoised adopting fast median filtering algorithm with mean acceleration. The fuzzing mathematics were not only developed with the aim of improving the visual quality of underwater sonar images, but also involved in the sonar image segmentation for extracting the target area from the suspicious area. Both the geometric and statistical features were treated as features in PCA algorithm. As demonstrated by the experimental results, visual quality of the sonar image was improved, multi-target threshold segmentation was achieved and multiple targets could be analyzed and clustered for stably tracking AUV.

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Abbreviations

\( x_{\text{max} } \) :

Maximum gray value of the image

\( x_{\text{min} } \) :

Minimum gray value of the image

\( f_{ij} \) :

Gray value of the pixel located at (ij)

\( \mu (f_{ij} ) \) :

Membership degree of the gray value at pixel (ij)

averagex(t):

Average of x position coordinate of the pixel whose gray value equals to the given threshold t

averagey(t):

Average of y position coordinate of the pixel whose gray value equals to the given threshold t

\( d_{\text{max} } \) :

Maximum distance from the spatial pixel to the average position

\( f_{\text{max} } \) :

Maximum gray value

\( f_{\text{min} } \) :

Minimum gray value

\( \mu_{0} \) :

Average value of the foreground pixels gray value

\( \mu_{1} \) :

Average value of the background pixels gray value

X :

A fuzzy set

T max :

Records the threshold value corresponding to the blur distance \( d_{\text{max} } \)

\( B(x,y) \) :

Binary image

\( P_{R} \) :

Perimeter

JM:

Compactness

L:

Major axis length

S:

Minor axis length

\( F_{1} \) :

The first principal component index

\( F_{2} \) :

Second principal component index

\( \lambda_{i} \) :

Coefficient of the principal component Fi

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Acknowledgements

This research was funded in part by the National Natural Science Foundation of China under Grants (51979057, 51609050), in part by the Research Fund from Science and Technology on Underwater Vehicle Technology (6142215180209), and in part by the Fundamental Research Funds for the Central Universities Facing International Academic Frontier Support Program (3072019CFG0101).

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Authors and Affiliations

  1. Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, 150001, China

    Mingwei Sheng, Songqi Tang, Lei Wan, Zhongben Zhu & Jun Li

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  1. Mingwei Sheng
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  2. Songqi Tang
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  3. Lei Wan
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  4. Zhongben Zhu
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  5. Jun Li
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Correspondence to Zhongben Zhu.

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Sheng, M., Tang, S., Wan, L. et al. Fuzzy Preprocessing and Clustering Analysis Method of Underwater Multiple Targets in Forward Looking Sonar Image for AUV Tracking. Int. J. Fuzzy Syst. 22, 1261–1276 (2020). https://doi.org/10.1007/s40815-020-00832-x

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