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结合多特征赋权的谱聚类水下多目标分割技术
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作者单位:

1.大理大学 工程学院;2.哈尔滨工程大学 物理与光电工程学院;3.云南民族大学 电气信息工程学院

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基金项目:

国家自然科学(61761048);云南省地方本科高校基础研究联合专项资金项目[2019FH001(-066)];黑龙江省自然科学基金(LC2018026)


Underwater multi-object segmentation technology based on spectral clustering with multi-feature weighting
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Affiliation:

1.School of Engineering,Dali University;2.College of Physics and Optoelectronic Engineering,Harbin Engineering University;3.School of Electrical and Information Technology,Yunnan Minzu University

Fund Project:

Natural Science Foundation of China (61761048);Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities’Association [2019FH001(-066)]; Natural Science Foundation of Heilongjiang Province ( LC2018026)

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    摘要:

    声呐图像具有受噪声污染严重的特点,导致水下多目标分割存在精度低的问题。为此,提出一种自调整谱聚类结合熵权法进行多特征赋权的水下多目标分割技术。该技术首先通过自调整谱聚类对声呐图像的像素点进行聚类处理,使图像划分为多个独立的区域,然后根据多特征的互补性和冗余性统计每个区域的信息熵、亮度、对比度、狭长度等特征,利用熵权法对多特征进行赋权并筛选出最优的一个目标区域,再将该最优目标区域和所有区域进行多特征的相似度匹配,最后根据相似度的匹配结果使用自适应阈值迭代法自动分割出所有的目标区域。实验结果表明没有对噪声干扰区域误分割,分割出的目标区域精度更高,验证了所提方法的有效性。

    Abstract:

    Sonar image is seriously polluted by noise, which leads to the problem of low precision in underwater multi-target segmentation.Therefore, an underwater multi-object segmentation technique based on self-adjusting spectrum clustering combined with entropy weight method is proposed.The technology first by self-tuning spectral clustering of sonar image pixel clustering processing, make the image is divided into multiple independent area, and then according to the characteristics of complementarity and more sections of the redundancy of the statistical information entropy characteristics, brightness, contrast, long and narrow degree, entropy weight method is used to analyse the characteristics more empowerment and the optimal selection of a target area,Then the optimal target region is matched with all regions by multi-feature similarity. Finally, all target regions are segmented automatically by adaptive threshold iterative method according to the matching results of similarity. Experimental results show that there is not over-segmented of noise interference regions, and target regions segmented have higher accuracy, which verifies the effectiveness of the proposed method.

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  • 收稿日期: 2021-11-24
  • 最后修改日期: 2022-03-18
  • 录用日期: 2022-03-21
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