Slice-Connection Clustering Algorithm for Tree Roots Recognition in Noisy 3D GPR Data

TL;DR

This paper introduces the Slice-Connection Clustering Algorithm (SCC) for accurately recognizing tree roots in noisy 3D GPR data, effectively separating roots from noise and other subsurface objects in real field conditions.

Contribution

The paper presents a novel SCC method that improves tree root recognition in noisy 3D GPR data by effectively filtering noise and distinguishing roots from other objects.

Findings

Successfully recognizes tree roots in noisy GPR data

Effectively filters out noise and non-root objects

Provides accurate 3D mapping of root locations

Abstract

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.