| 复杂环境下地基SAR粗差探测及应用 |
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| 引用本文： | 马金玉,龙四春,童爱霞,吴文豪,祝传广.复杂环境下地基SAR粗差探测及应用[J].测绘通报,2020,0(2):43. |
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| 作者姓名： | 马金玉 龙四春 童爱霞 吴文豪 祝传广 |
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| 作者单位： | 1. 湖南科技大学煤炭资源清洁利用与矿山环境保护湖南省重点实验室, 湖南 湘潭 411201;2. 湖南科技大学资源环境与安全工程学院, 湖南 湘潭 411201 |
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| 基金项目： | 国家自然科学基金（41877283；41474014） |
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| 摘 要： | 雷达视线受施工机械等的间断遮挡导致部分影像产生相位奇异值，从而造成解缠错误及误差传递，简单的相关影像处理难以识别受遮挡影像。本文提出了改进的基于小波变换的信号奇异性检测方法，通过对地基SAR PS点时序相位特征进行分析，将遮挡影像识别转化为粗差探测问题；由PS点相位序列与影像位置关系，根据测区PS点相位序列的奇异点集合得到受遮挡影像集；最后将受遮挡影像剔除后得到的地基SAR监测结果与精密全站仪、水准与游标卡尺数据进行对比分析。结果表明：本文提出的方法用于受遮挡影像的识别是可行的，解决了地基SAR在实际工程应用中可能存在的影像遮挡带来的测量数据含有粗差的问题，提高了监测区域伪影像检测的效率与准确性。
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| 关 键 词： | GBSAR 变形监测 粗差探测 小波变换 奇异性检测 |
| 收稿时间： | 2019-07-09 |
Gross error detection and application of ground-based SAR in complex environment |
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| MA Jinyu,LONG Sichun,TONG Aixia,WU Wenhao,ZHU Chuanguang.Gross error detection and application of ground-based SAR in complex environment[J].Bulletin of Surveying and Mapping,2020,0(2):43. |
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| Authors: | MA Jinyu LONG Sichun TONG Aixia WU Wenhao ZHU Chuanguang |
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| Institution: | 1. Hunan Province Key Laboratory of Coal Resources Clean-utilization and Min Environment Protection, Hunan University of Science and Technology, Xiangtan 411201, China;2. School of Resources Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China |
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| Abstract: | The intermittent occlusion of radar line of sight by construction machinery leads to the phase singular value of some images, resulting in unwrapping errors and error transmission, and it is difficult to identify the occluded images by simple related image processing. This paper proposes an improved method of signal singularity detection based on wavelet transform. Through the analysis of the temporal phase characteristics of the ground-based SAR PS points, the occlusion image recognition is transformed into the problem of gross error detection. According to the relationship between the PS point phase sequence and the image position, the occluded image set is obtained according to the singular point set of the PS point phase sequence in the survey area. Finally, the ground-based SAR monitoring results obtained by removing the occluded image are compared and analyzed with the precision total station, level and vernier caliper data. The results show that the method proposed in this paper is feasible for the recognition of occluded images. It solves the problem of gross error in the measurement data caused by the possible image occlusions in the practical engineering application of ground-based SAR, and improves the efficiency and accuracy of the detection of false images in the monitoring area. |
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| Keywords: | GBSAR deformation monitoring gross error detection wavelet transformation singularity detection |
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