Sparsity enhanced MRF algorithm for automatic object detection in GPR imagery
Publication Name
Mathematical Biosciences and Engineering
Abstract
This study addressed the problem of automated object detection from ground penetrating radar imaging (GPR), using the concept of sparse representation. The detection task is first formulated as a Markov random field (MRF) process. Then, we propose a novel detection algorithm by introducing the sparsity constraint to the standard MRF model. Specifically, the traditional approach finds it difficult to determine the central target due to the influence of different neighbors from the imaging area. As such, we introduce a domain search algorithm to overcome this issue and increase the accuracy of target detection. Additionally, in the standard MRF model, the Gibbs parameters are empirically predetermined and fixed during the detection process, yet those hyperparameters may have a significant effect on the performance of the detection. Accordingly, in this paper, Gibbs parameters are self-adaptive and fine-tuned using an iterative updating strategy followed the concept of sparse representation. Furthermore, the proposed algorithm has then been proven to have a strong convergence property theoretically. Finally, we verify the proposed method using a real-world dataset, with a set of ground penetrating radar antennas in three different transmitted frequencies ( 50 MHz, 200 MHz and 300 MHz). Experimental evaluations demonstrate the advantages of utilizing the proposed algorithm to detect objects in ground penetrating radar imagery, in comparison with four traditional detection algorithms.
Open Access Status
This publication may be available as open access
Volume
20
Issue
9
First Page
15883
Last Page
15899
Funding Number
SK2017A0098
Funding Sponsor
National Natural Science Foundation of China