Abstract
As a nondestructive geophysical tool, Ground penetrating radar (GPR) has been applied in tree root study in recent years. With increasing amounts of GPR data collected for roots, it is imperative to develop an efficient automatic recognition of roots in GPR images. However, few works have been completed on this topic because of the complexity in root recognition problem. Based on GPR datasets from both controlled and in situ experiments, the randomized Hough transform (RHT) algorithm was evaluated in root object recognition for different center frequencies (400 MHz, 900 MHz, and 2000 MHz) in this paper. Reasonable accuracy was obtained (both a high recognition rate and a low false alarm rate) in these datasets, which shows it is feasible to apply the RHT algorithm for root recognition. Furthermore, we evaluated the influence of root and soil factors on the recognition. We found that the performance of RHT algorithm is mainly affected by root interval length, root orientation, and clutter noise of soil. The recognition results by RHT could be applied for large scale root system distribution study in belowground ecology. Further studies should be conducted to reduce clutter noise and improve the recognition of the complex root reflections.
Highlights
Ground penetrating radar (GPR), a nondestructive geophysical technique, has been widely used in detecting underground objects such as soil horizons, bedrocks, water tables, pipes, cables, and buried artifacts [1,2,3]
The artificial interpretation method especially cannot meet the requirement of huge amounts of GPR data processing for large scale, whole-root system distribution of plant community in field conditions [9], which is significant in belowground ecology
As a part of a long-term research on root detection based on GPR, this paper presents a detailed study on automatic recognition of root objects in GPR images using randomized Hough transform (RHT)
Summary
Ground penetrating radar (GPR), a nondestructive geophysical technique, has been widely used in detecting underground objects such as soil horizons, bedrocks, water tables, pipes, cables, and buried artifacts [1,2,3]. Raw GPR radargrams provide insufficient geometrical information of buried targets and are always disturbed by soil clutter noise. Locating and identifying root objects in GPR radargrams is a prerequisite step in GPR data processing. The artificial interpretation method especially cannot meet the requirement of huge amounts of GPR data processing for large scale, whole-root system distribution of plant community in field conditions [9], which is significant in belowground ecology. It is imperative to explore an effective and accurate automatic method for identifying root objects in GPR images.
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