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1.首都师范大学资源环境与旅游学院,北京,100048
2.中国测绘科学研究院,北京,100036
3.首都师范大学城市环境过程与数字模拟国家重点实验室培育基地,北京,100048
4.首都师范大学三维信息获取与应用教育部重点实验室,北京,100048
5.中国矿业大学自然资源部国土环境与灾害监测重点实验室,江苏 徐州,221116
Received:28 August 2024,
Published:05 August 2025
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纪方方,余洁,卢丽君,等.层次化的城区机载阵列干涉SAR点云噪声抑制[J].武汉大学学报(信息科学版),2025,50(8):1555-1570.
JI Fangfang,YU Jie,LU Lijun,et al.A Hierarchical Urban Airborne ArrayInSAR Point Cloud Noise Suppression Method[J].Geomatics and Information Science of Wuhan University,2025,50(8):1555-1570.
纪方方,余洁,卢丽君,等.层次化的城区机载阵列干涉SAR点云噪声抑制[J].武汉大学学报(信息科学版),2025,50(8):1555-1570. DOI:10.13203/j.whugis20240240
JI Fangfang,YU Jie,LU Lijun,et al.A Hierarchical Urban Airborne ArrayInSAR Point Cloud Noise Suppression Method[J].Geomatics and Information Science of Wuhan University,2025,50(8):1555-1570. DOI:10.13203/j.whugis20240240
在机载阵列干涉合成孔径雷达系统(array interferometric synthetic aperture radar
ArrayInSAR)数据成像过程中,由于受到系统热噪声、通道幅度-相位不一致、电磁波多路径干扰等因素影响,机载ArrayInSAR点云中存在大量噪声,严重影响三维重建精度。在分析了机载ArrayInSAR点云噪声特征的基础上,提出一种层次化的机载ArrayInSAR点云去噪方法。首先根据机载ArrayInSAR点云中噪声点与非噪声点的分布对噪声进行类别划分,然后集成基于自适应阈值的混合滤波算法、基于法向量和曲率改进的双边滤波算法、基于面状信息拟合的多路径干扰噪声去除算法,实现城区机载ArrayInSAR点云层次化噪声抑制。为验证有效性,将所提方法与其他经典去噪方法进行了效果比较,结果表明,所提方法在去噪完整度、正确率和质量评价指标等方面均优于经典方法,证明所提方法能提高机载ArrayInSAR点云精度,为基于机载ArrayInSAR点云的三维重建提供高质量数据。
Objectives
2
In the process of data imaging in the airborne array interferometric synthetic aperture radar (ArrayInSAR)
due to the influence of system thermal noise
channel amplitude-phase inconsistency
electromagnetic multipath interference and other factors
there is a lot of noise in the airborne ArrayInSAR point cloud
which seriously affects the three-dimensional reconstruction accuracy based on ArrayInSAR point cloud.
Methods
2
Based on the analysis of the noise characteristics of the airborne ArrayInSAR point cloud
we proposed a hierarchical airborne ArrayInSAR point cloud noise suppression(HPCNS)method. First
according to the distribution of noise points and non-noise points in the ArrayInSAR point cloud
the noise is divided into outlier noise
channel amplitude-phase inconsistency errors noise and multipath interference noise. Then
a hybrid filtering algorithm based on adaptive threshold
a bilateral filtering algorithm based on normal vector and curvature improvement
and a multipath noise removal algorithm based on surface information fitting are integrated to process the three kinds of noise
and the hierarchical noise suppression of the airborne ArrayInSAR point cloud in urban areas is realized.
Results
2
In order to verify the effectiveness of HPCNS method
the denoising effect of HPCNS method is compared with other classical denoising methods. The results show that HPCNS method is superior to other classical denoising methods in the aspects of denoising integrity
accuracy and quality evaluation index.
Conclusions
2
The hierarchical ArrayInSAR point cloud noise suppression method can better remove various types of noise in the ArrayInSAR point cloud data
significantly improve the quality of the airborne ArrayInSAR point cloud
provide high-quality data for three-dimensional reconstruction based on the airborne ArrayInSAR point cloud
and better data support for intelligent mapping.
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