Volume 41, Number 1, February 2023
|153 - 159
|02 June 2023
Sonar image target detection based on multi-region optimal selection strategy
School of Engineering, Dali University, Dali 671003, China
2 School of Future Technology, Harbin Engineering University, Harbin 150001, China
3 School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China
To overcome the adverse effects of noise and shadow regions on target detection in side-scan sonar images, more precisely, it is difficult to accurately detect targets, a target detection technology based on a multi-region optimal selection strategy of spectral clustering combined with the entropy weight method is proposed in this study. First, the cluster numbers for spectral clustering are set in advance based on prior knowledge, and the pixels of the sonar image are clustered into several different regions. Second, the invariable features of translation, rotation and scaling up that each region is extracted and used to construct the feature criterion matrix for the multiple regions. Last, the entropy weight method is used to calculate the weights of each feature and the comprehensive weighted score of each region for this feature criterion matrix to obtain the final target region. Experimental results show that the proposed method can effectively overcome the adverse effects of noise and shadow regions in side-scan sonar images, but also achieve the selection of optimal target region among multiple regions after image clustering, thus verifying the feasibility and effectiveness of the proposed method in this study.
为了解决侧扫声呐图像目标检测受噪声和阴影区域影响, 难以准确检测目标的问题, 提出一种谱聚类结合熵权法的多区域最优选择策略的目标检测方法。根据先验知识提前设定谱聚类的聚类数, 将声呐图像的像素聚类为多个不同的区域; 提取每个区域具有的平移、旋转和缩放的不变性特征, 用于构建多区域的特征准则矩阵; 利用熵权法对该特征准则矩阵计算各特征的权重以及每个区域的综合加权分数, 即可得到最终的目标区域。实验结果表明, 所提方法不仅能够有效地克服侧扫声呐图像中的噪声和阴影区域带来的不利影响, 还可以在图像聚类后的多个区域中实现最优目标区域的选择, 验证了所提方法的可行性和有效性。
Key words: sonar image / objective / image segmentation / spectral clustering / feature selection / entropy weight method
关键字 : 声呐图像 / 目标 / 图像分割 / 谱聚类 / 特征选择 / 熵权法
© 2023 Journal of Northwestern Polytechnical University. All rights reserved.
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