论文标题
噪音3D GPR数据中的树根识别的切片连接聚类算法
Slice-Connection Clustering Algorithm for Tree Roots Recognition in Noisy 3D GPR Data
论文作者
论文摘要
树根的3D映射是一种流行的接地雷达（GPR）应用。在实际野外测试中，由于噪声反射模式的识别，其噪声反射模式受到了不感兴趣的地下目标，例如岩石，腔，土壤不相等等。将切片连接群集算法（SCC）应用于重新构造的3D图像中的彼此中彼此感兴趣的区域。所提出的方法可以成功识别根的雷达特征，并将根部与其他对象区分开。同时，通过我们的方法忽略了大多数噪声雷达功能。通过该方法获得的雷达图的最终3D映射可用于估计树根的位置和扩展趋势。在实际GPR数据上测试了拟议系统的有效性。
3D mapping of tree roots is a popular ground-penetrating radar (GPR) application. In real field tests, the recognition of tree roots suffers due to noisey reflection patterns from subsurface targets that are not of interest, such as rocks, cavities, soil unevenness, etc. A Slice-Connection Clustering Algorithm (SCC) is applied to separate the regions of interest from each other in a reconstructed 3D image. The proposed method can successfully recognize the radar signatures of the roots and distinguish roots from other objects. Meanwhile, most noise radar features are ignored through our method. The final 3D mapping of the radargram obtained by the method can be used to estimate the location and extension trend of the tree roots. The effectiveness of the proposed system is tested on real GPR data.
