顾及协方差函数的自适应四叉树InSAR数据压缩算法

测绘学报 ›› 2014, Vol. 43 ›› Issue (5): 486-492.

顾及协方差函数的自适应四叉树InSAR数据压缩算法

张静¹,张勤²,赵超英³,张菊清⁴

1. 长安大学
2. 长安大学地测学院
3. 长安大学地质工程与测绘学院测绘科学与工程系
4. 长安大学地质工程与测绘学院

收稿日期:2012-12-21 修回日期:2014-02-19 出版日期:2014-05-20 发布日期:2014-06-05
通讯作者: 张勤 E-mail:zhangqinle@263.net.cn
基金资助:
多分辨率雷达干涉融合技术用于矿区塌陷灾害研究;基于时空信息的InSAR数据质量控制与后处理研究;汾渭盆地重点地区地面沉降地裂缝InSAR与GPS监测;地震行业科研专项项目-鄂尔多斯地块周缘InSAR变形检测及大地测量数据管理系统建设

A Quadtree InSAR Data Reduction Method Based on Covariance Function

Received:2012-12-21 Revised:2014-02-19 Online:2014-05-20 Published:2014-06-05

摘要/Abstract

摘要：

利用InSAR数据进行形变机理反演时，由于InSAR数据点众多，且含有较多的误差乃至粗差点，严重制约了反演的效率和可靠性。为此，本文提出了顾及InSAR数据物理空间相关性来设立协方差函数，并依据协方差函数确定四叉树象限分解阈值和最大象限大小的自适应四叉树分解InSAR数据压缩算法。本算法能够在尽可能保留形变信号特征细节信息的同时，极大地降低InSAR数据量。本文以西安地区地面沉降InSAR结果为例进行了实验分析，验证了该算法的有效性，结果表明该方法能够在不损失形变信号特征的同时，有效的实现InSAR数据压缩和噪声消除的目的。

关键词: 四叉树, 数据压缩, 空间相关, 协方差函数

Abstract:

A major problem in inversion of deformation mechanism using InSAR data is that the InSAR results often contain thousands to millions of data points. Furthermore, there always exist errors and even some blunders, which make the data inversion be lower efficient and lower reliable. Thus, we propose an adaptive quadtree decomposition method for InSAR data reduction in order to reduce the data numbers without losing the significant information about the deformation. The two important parameters of quadtree decomposition by covariance function is determined ,which is eatablished by taking account of the physical spatial crrelation of InSAR data. The algorithm can preserve details of deformation as much as possible and achieve efficient data reduction. This method is evaluated with InSAR data over Xi’an land subsidence. The results indicate that the algorithm proposed in this manuscript can not only reduce InSAR data number efficiently under a very good preservation of deformation signal, but can eliminate the noise of deformation results efficiently.

Key words: quadtree, data reduction, spatial correlation, covariance function

中图分类号:

P237

张静张勤赵超英张菊清. 顾及协方差函数的自适应四叉树InSAR数据压缩算法[J]. 测绘学报, 2014, 43(5): 486-492.

参考文献

Ferretti, A., Monti, G. A., Prati, C., Rocca, F., & Massonnet, D. InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation(TM-19, February 2007). ESA Publications. ESTEC, Postbus 299, 2200 AG Noordwijk, the Netherlands
Zhang Jing, Zhao Chaoying, et al. Research on Deformation Contour Mapping from Interferometry[J]. Journal of Geodesy and Geodynamcs,2009,V29(5):143-146.(张静,赵超英,张勤,瞿伟.InSAR形变等值线图绘制方法研究[J].大地测量与地球动力学,2009,V29(5):143-146.)
Du Xutao. Discussed on the image compression algorithm based on transformation[J]. Software Guide, 2012，V11(1):145-148.(独旭涛. 基于变换的图像压缩算法探讨[J]. 软件导刊，2012，V11(1):145-148.)
Li Sha, Huang Lin, et al. SAR Image Compression Algorithm Based on Wavelet Transform[J]. Computer Measurement & Control, 2012，V20（8）：2310-2313.(李莎，黄琳等. 基于小波变换的SAR图像压缩算法研究[J]. 计算机测量与控制，2012，V20（8）：2310-2313.)
Xie Haihui. Speckle Reduction and Data Compression in SAR Images Using Wavelet-Based Method[D]. University of Electronic Science and Technology, 2004.(谢海慧. SAR相干斑抑制及图像压缩的小波域方法[D]. 电子科技大学，2004.)
Pritchard, M. E., M. Simons, P. A. Rosen, S. Hensley, and F. H. Webb (2002), Co-seismic slip from the 1995 July 30 Mw = 8.1 Antofagasta, Chile, earthquake as constrained by InSAR and GPS observations, Geophys. J. Int. , 150 , 362 – 376
Simons, M ., Y. Fialko, and L . Rivera ( 2002 ), Coseismic deformation from t he 1999 Mw 7.1 Hector Mine, California, earthquake as inferred from InSAR and GPS observations, Bull. Seismol. Soc. Am., 92 , 1390 – 1402
Jonsson, S. (2002), Modeling volcano and earthquake deformation from satellite radar interferometric observations, Ph.D. thesis, Stanford Univ. , Stanford, Calif.
Lohman, R. B., and M. Simons (2005), Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling,Geochem. Geophys. Geosyst. , 6, Q01007, doi:10.1029/2004GC000841.
Ni Lin. Compression of Remote Sensing Images Based on Adaptive Quadtree Partitioning [J].Journal of Remote Sensing, 2002, 6(5):343-351.(倪林.基于自适应四叉树分割的遥感图像压缩算法[J]. 遥感学报，2002,6(5):343-351.)
Knospe, S. H.-G., and S. Jònsson (2010), Covariance estimation for dInSAR surface deformation measurements in the pressence of anisotropic atmospheric noise, IEEE Trans. Geosci. Remote Sens., 48, 2057–2065.

[1]	袁博. 基于混合像元空间与谱间相关性模型的NMF线性盲解混[J]. 测绘学报, 2019, 48(9): 1151-1160.
[2]	信睿, 艾廷华. Gosper曲线支持的正六边形栅格数据游程编码及高效压缩[J]. 测绘学报, 2019, 48(2): 226-237.
[3]	刘照欣, 赵辽英, 厉小润, 陈淑涵. 高光谱亚像元定位的线特征探测法[J]. 测绘学报, 2019, 48(11): 1464-1474.
[4]	王乐洋, 陈汉清. 多波束测深数据处理的抗差最小二乘配置迭代解法[J]. 测绘学报, 2017, 46(5): 658-665.
[5]	胡佰林, 王春波, 赵学胜, 崔马军. 基于WGS-84椭球面的退化四叉树经纬线格网剖分[J]. 测绘学报, 2016, 45(S1): 32-39.
[6]	喻金桃, 郭海涛, 李传广, 卢俊, 姜春雪. 四叉树与多种活动轮廓模型相结合的遥感影像水边线提取方法[J]. 测绘学报, 2016, 45(9): 1104-1114.
[7]	张翔, 王少东, 王玉霞. 基于偏移四叉树投票的“大尺寸”点状符号多尺度无压盖可视化[J]. 测绘学报, 2016, 45(8): 983-991.
[8]	王宇谱, 吕志平, 王宁, 李林阳, 宫晓春. 顾及卫星钟随机特性的抗差最小二乘配置钟差预报算法[J]. 测绘学报, 2016, 45(6): 646-655.
[9]	王少宇, 焦洪赞, 钟燕飞. 条件随机场模型约束下的遥感影像模糊C-均值聚类算法[J]. 测绘学报, 2016, 45(12): 1441-1447.
[10]	郭海涛, 孙磊, 申家双, 陈小卫, 张宏伟. 一种四叉树和测地线活动轮廓模型相结合的海陆影像分割方法[J]. 测绘学报, 2016, 45(1): 65-72.
[11]	张盈, 张景雄. 顾及空间相关性的遥感影像信息量的度量方法[J]. 测绘学报, 2015, 44(10): 1117-1124.
[12]	王慧贤江万寿雷呈强张一鸣. 一种基于光谱与空间局部相关的SVR影像融合方法[J]. , 2013, 42(4): 0-0.
[13]	刘繁明张迎发荆心. 基于压缩感知的地磁数据分频压缩与重构方法研究[J]. , 2013, 42(4): 0-0.
[14]	赵学胜,范德芹,王娇娇,王磊. 退化四叉树格网的全球多分辨率DEM无缝表达[J]. 测绘学报, 2012, 41(6): 918-925.
[15]	刘扬,宫阿都,李京. 基于数据分层分块的海量三维地形四叉树简化模型[J]. 测绘学报, 2010, 39(4): 0-440.