| 基于多标签层次聚类的GPR 图像双曲波提取方法 |
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| 作者姓名： | 李文生 原 达 苗 翠 王冬雨 |
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| 作者单位： | (山东工商学院智能信息处理重点实验室，山东 烟台 264005) |
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| 基金项目： | 山东省重点研发计划项目(2019GGX101040) |
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| 摘 要： | 探地雷达(GPR)图像双曲波提取是分析地下目标位置和结构的重要方法，但在真
实环境中，由于噪声和杂波的干扰，使得提取出的双曲波存在结构不完整、碎片化和形状异
常等问题，不利于数据分析和三维建模等后续操作。为此，提出了一种基于多标签层次聚类
的双曲波提取方法(MHCE)。首先通过信息熵评价像素邻域的稳定性，构造了基于信息熵的
距离度量来进行层次聚类；然后利用聚类后的邻接空间进行多标签聚类以降低杂波和噪声对
双曲波提取的影响；最后结合多标签聚类结果的拟合形状和纹理方向提取双曲波。实验表明，
该方法对于真实GPR 图像双曲波具有较好的鲁棒性，能够获得规范化的双曲波形状和位置
参数。
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| 关 键 词： | 探地雷达图像 双曲波 信息熵 多标签层次聚类 鲁棒性 |
Hyperbola extraction method based on multi-label hierarchical clustering algorithm of GPR images |
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| Authors: | LI Wen-sheng YUAN Da MIAO Cui WANG Dong-yu |
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| Affiliation: | (Key Laboratory of Intelligent Information Processing, Shandong Technology and Business University, Yantai Shandong 264005, China) |
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| Abstract: | Hyperbola extraction in ground penetrating radar (GPR) images is an important feature to
analyze the location and structure of underground objects. However, there are often some problems
with the extracted hyperbola, such as incomplete structure, fragmentation, and shape anomalies,
caused by the interference of noise and clutter that are typical of real environments. These issues are
not conducive to the subsequent quantitative operations, such as data analysis and 3D modeling. In
this context, this paper proposed a multi-label hierarchical clustering-based hyperbola extraction
method (MHCE) for the hyperbola extraction of GPR images. Firstly, through evaluating the stability
between pixel neighborhoods by the means of information entropy, an information entropy-based
distance method was constructed to conduct the hierarchical clustering algorithm. Next, a multi-label
clustering method was proposed based on the adjacency space of the clustering results, so as to reduce
the influence of clutter and noise on hyperbola extraction. Finally, the hyperbola was extracted
combined with the fitting shape and texture orientation of the multi-label clustering results. The
experimental results show that this method is robust for GPR images and can be used to obtain the
shape and position parameters of a normalized hyperbola. |
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| Keywords: | ground penetrating radar image hyperbola information entropy multi-label hierarchical clustering algorithm robustness |
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