基于MATLAB的多种插值算法在地表时序监测中的应用研究
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摘要
为了克服InSAR技术获取数据时,由于部分月份缺失影像,而导致对研究区进行长时间序列形变分析时,所获取的形变序列为非等时距,不利于系统地反映其形变趋势的问题。本文基于MATLAB软件进行插值算法编写,对缺失时间跨度同的两种地表形变监测数据,分别进行多种插值算法实验。实验结果显示:对于缺失月份较少的数据,三次样条插值拟合效果最好;对缺失较多月份或分布不均匀的数据,立方插值拟合效果最好;线性插值和邻近点插值的拟合效果较差,不适用于对缺失的数据进行插值。该结果对由于缺失部分数据而影响其形变时序分析的情况提供了方法指导,具有较强的实用意义。
In order to overcome the InSAR technology acquisition data, due to the lack of images in some months,the long-term sequence deformation analysis of the study area, the obtained deformation sequence was non-equal time interval, which is not conducive to systematically reflect the deformation trend. In this paper, based on MATLAB software, the interpolation algorithm was written. For the two kinds of surface deformation monitoring data with the same time span, a variety of interpolation algorithm experiments were carried out. The experimental results showed that for the missing data, the cubic spline interpolation has the best effect. For the data with more months or uneven distribution, the cubic interpolation fits best; linear interpolation and adjacent point interpolation Poor effect, not suitable for interpolating missing data. This result provides a methodological guidance for the situation that affects the deformation timing analysis due to the missing part of the data,and has a strong practical significance.
In order to overcome the InSAR technology acquisition data, due to the lack of images in some months,the long-term sequence deformation analysis of the study area, the obtained deformation sequence was non-equal time interval, which is not conducive to systematically reflect the deformation trend. In this paper, based on MATLAB software, the interpolation algorithm was written. For the two kinds of surface deformation monitoring data with the same time span, a variety of interpolation algorithm experiments were carried out. The experimental results showed that for the missing data, the cubic spline interpolation has the best effect. For the data with more months or uneven distribution, the cubic interpolation fits best; linear interpolation and adjacent point interpolation Poor effect, not suitable for interpolating missing data. This result provides a methodological guidance for the situation that affects the deformation timing analysis due to the missing part of the data,and has a strong practical significance.
引文
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[2]杨力,钱博,冯永新,薄煜明.提高BOC调制信号多相关峰值精度的三次样条插值法[J].系统仿真学报,2009,21(12):3573-3576.
[3]吕建升,须鼎兴.基于离散点绘制等高线算法的研究[J].现代测绘, 2006(05):5-7.
[4]聂磊,舒红,刘艳.复杂地形地区月平均气温(混合)地理加权回归克里格插值[J].武汉大学学报(信息科学版),2018(10):1553-1559.
[5]李雨,崔希民,何荣,李春意.基于Matlab地表沉陷预测的自动实现[J].煤炭技术, 2018, 37(09):147-149.
[6]苏超威,丁克良,周命端,刘淼.结合三次样条插值函数的地铁地表沉降监测预报模型[J].测绘通报,2015(S1):160-162.
[7]余礼仁,徐良骥,庞会,王振兵.融合三次样条插值的D-InSAR沉陷变形监测技术[J].测绘通报,2017(09):51-55.
[8]雒明世,张倩琳.基于MATLAB的OFDM系统仿真与教学研究[J].软件, 2015, 36(6):152-157.
[9]王赛男,邢冬梅.基于MATLAB的小波阈值滤波与应用[J].软件, 2015, 36(10):102-104.
[10]于洋,袁健华,钱江,等.新边界条件下的三次样条插值函数[J].软件, 2016, 37(02):25-28.
[11]姜文涛,刘荣海,杨迎春,等.基于MATLAB时频分析算法的滚动轴承故障研究[J].软件, 2018, 39(2):102-107.
[12]孙彦超,章坚民.基于MATLAB的DG选址定容可视化系统的设计与实现[J].软件, 2018, 39(4):142-147.
[13]张梦远,姚瑶.吉林省气温与降水数据空间插值与分布研究[J].吉林农业, 2018(20):108-110.
[14]王巍,徐媛媛,杨皓,何雍春,黄孟佳,杨正琳,袁军.基于类线性插值算法的DDFS设计与实现[J].微电子学, 2018,48(03):406-410.
[15]陈爽,张悦,张勤俭,曹宇男.一种基于B样条插值的机器人速度规划算法[J].应用基础与工程科学学报,2018,26(03):661-671.
[16]王玉春,王昆,阳海鹏,欧阳群,张其大.几种插值和拟合算法在潮汐插值上的应用分析[J].科技创新导报,2018,15(08):114-115.