| 结合正则化K-SVD和Hampel滤波的探地雷达数据重建 |
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| 引用本文： | 闫坤,张志华,颜鲁春.结合正则化K-SVD和Hampel滤波的探地雷达数据重建[J].测绘通报,2021,0(8):22-27. |
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| 作者姓名： | 闫坤 张志华 颜鲁春 |
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| 作者单位： | 兰州交通大学测绘与地理信息学院,甘肃 兰州730070;地理国情监测技术应用国家地方联合工程研究中心,甘肃 兰州730070;甘肃省地理国情监测工程实验室,甘肃 兰州730070;甘肃恒路交通勘察设计院有限公司,甘肃 兰州730070 |
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| 基金项目： | 国家自然科学基金（41861059）；兰州交通大学优秀平台（201806） |
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| 摘 要： | 为减弱因地形起伏造成的探地雷达数据间的能量差异，保证探地雷达图像解译和识别的准确性，本文提出了一种正则化K-SVD字典学习和Hampel滤波算法相结合的探地雷达数据重建方法。试验采用正则化K-SVD字典学习对探地雷达信号进行能量均衡，利用Hampel滤波算法剔除均衡后的信号异常值，并对均衡后的信号进行二维可视化，从而完成探地雷达图像重建。对比试验表明，本文方法不但可以均衡原始的探地雷达信号，而且其均衡后的信号更加符合探地雷达信号传播规律，可以保证单道数据信号的质量；其重建的图像效果更好，在探地雷达图像重建方面具有较好的实用价值。
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| 关 键 词： | 能量均衡 正则化K-SVD字典学习 Hampel滤波算法 配准法 |
| 收稿时间： | 2020-08-24 |
Ground penetrating radar data reconstruction combined with regularized K-SVD and Hampel filter |
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| YAN Kun,ZHANG Zhihua,YAN Luchun.Ground penetrating radar data reconstruction combined with regularized K-SVD and Hampel filter[J].Bulletin of Surveying and Mapping,2021,0(8):22-27. |
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| Authors: | YAN Kun ZHANG Zhihua YAN Luchun |
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| Institution: | 1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China;2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China;3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China;4. Gansu Heng Lu Traffic Survey and Design Institute Co., Ltd., Lanzhou 730070, China |
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| Abstract: | To alleviate the energy difference between the GPR data caused by topographic relief, and ensure the accuracy of the GPR image interpretation and recognition, this paper proposes a GPR data reconstruction method combining regularized K-SVD dictionary learning and Hampel filtering algorithm. In the experiment, regularized K-SVD dictionary learning is used to carry out the energy balance of GPR signals, Hampel filtering algorithm is used to eliminate the outliers of the balanced signals, and two-dimensional visualization of the balanced signals is carried out, so as to complete the image reconstruction of the GPR. The comparison experiment shows that this method can not only balance the original GPR signal, but also the balanced signal is more in line with the GPR signal propagation law, which can guarantee the quality of the trace data signal, and the reconstructed image is better, which has a good practical value in the GPR image reconstruction. |
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| Keywords: | energy balance regularized K-SVD dictionary learning Hampel filtering algorithm registration method |
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