Real-Time Hyperbola Recognition and Fitting in GPR Data
Real-Time Hyperbola Recognition and Fitting in GPR Data
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DOI：
10.1109/tgrs.2016.2592679
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发表时间：
2017-01-01
影响因子：
8.2
通讯作者：
Cohn, Anthony G.
中科院分区：
文献类型：
--
作者：
Dou, Qingxu;Wei, Lijun;Cohn, Anthony G.
针对探地雷达（GPR）图像中双曲线的自动识别与拟合问题，提出了一种适合于现场实时应用的计算方法。在对输入的探地雷达图像进行预处理后，采用一种新的阈值法将感兴趣区域从背景中分离出来。一种新的列连接聚类（C3）算法，然后应用到分离的感兴趣的区域彼此。随后，应用机器学习模型来从C3算法的输出识别双曲线签名，并且用正交距离双曲线拟合算法将双曲线拟合到每个这样的签名。新的聚类算法C3是该系统的核心组成部分，它可以识别双曲线签名和双曲线拟合。在机器学习算法中只使用了两个特征，这很容易使用少量的训练数据进行训练。提出了一种适用于“南开”双曲线的正交距离双曲线拟合算法，该算法比代数双曲线拟合算法具有更高的精度和鲁棒性。所提出的方法可以成功地识别和拟合双曲线签名与其他交叉，双曲线签名的失真，和不完整的双曲线签名的一个腿完全或大部分错过。作为一个新的贡献，推导出直接从一组给定的点计算初始“南开”双曲线的公式，这使得系统更有效。通过将双曲线拟合到双曲线签名而获得的参数是非常重要的特征;它们可以用于估计相关目标对象的位置和大小以及电磁波在介质中的平均传播速度。该系统的有效性进行了测试合成和真实的探地雷达数据。
The problem of automatically recognizing and fitting hyperbolae from ground-penetrating radar (GPR) images is addressed, and a novel technique computationally suitable for real-time on-site application is proposed. After preprocessing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm, and a hyperbola is fitted to each such signature with an orthogonal-distance hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal-distance hyperbola fitting algorithm for "south-opening" hyperbolae is introduced in this work, which is more robust and accurate than algebraic hyperbola fitting algorithms. The proposed method can successfully recognize and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions, and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulas to compute an initial "south-opening" hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting hyperbolae to hyperbolic signatures are very important features; they can be used to estimate the location and size of the related target objects and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data.