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遥感技术与应用  2013, Vol. 28 Issue (2): 258-262    DOI: 10.11873/j.issn.1004-0323.2013.2.258
图像与数据处理     
基于迭代高斯模型的干涉DEM滤波算法
王峰1,2,3,尤红建1,2,傅兴玉1,2
(1.中国科学院空间信息处理与应用系统技术重点实验室,北京 100190;
2.中国科学院电子学研究所,北京 100190;3.中国科学院大学,北京 100049 )
An InSAR DEM Speckle Filtering Algorithm based on Iterative Gaussian Model
Wang Feng1,2,3,You Hongjian1,2,Fu Xingyu1,2
(1.Key Laboratory of Technology in Geo-spatial Information Processing and Application System,IECAS,
Beijing 100190,China;2.Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China;
3.University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF(1174 KB)  
摘要:

提出了一种基于迭代高斯模型的干涉DEM滤波算法。该算法首先对含有噪声的干涉SAR高程数据进行噪声检测,对不符合高斯模型分布的数据,标记为噪声点,建立噪声标记矩阵;然后对标记为噪声的点,依据其邻域窗口内数据的统计特性,采用曲面拟合算法估计得出噪声点处的真实高程值。通过实际DEM的实验结果表明:该算法在有效消除噪声点的同时,可以较好地保持地面的高程值;与传统的低通滤波、中值滤波、Sigma滤波的实验结果相比,该算法的滤波结果较为理想。〖JP〗

关键词: 干涉DEM滤波噪声标记矩阵迭代高斯模型曲面拟合    
Abstract:

A speckle noise filtering algorithm based on iterative Gaussian model is proposed.Firstly,the Noise Marker Matrix (NMM) is calculated using the DEM data,where the pixels are not fit the Gaussian distribution were marked as noise.And then,the true height value can be estimated using the quadratic surface fitting based on the statistical properties of its neighborhood window.Finally,the proposed approach is carried out by InSAR DEM data.The results on actual DEM data show that the proposed algorithm can filter the noise data without disturbing the correct data.In comparison,the filtering results of the low-pass filter,the median filter and the sigma filter algorithm,the proposed algorithm can get the best filtering result.

Key words: InSAR DEM filtering    Noise Marker Matrix (NMM)    Iterative Gaussian model    Surface Fitting
收稿日期: 2012-03-23 出版日期: 2013-06-24
:  P 237.3  
基金资助:

国家863计划项目(2007AA120302)支持。

作者简介: 王峰(1988-),男,山东日照人,博士研究生,主要从事遥感图像处理研究。Email:wfeng_gucas@126.com。
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引用本文:

王峰,尤红建,傅兴玉. 基于迭代高斯模型的干涉DEM滤波算法[J]. 遥感技术与应用, 2013, 28(2): 258-262.

Wang Feng,You Hongjian,Fu Xingyu. An InSAR DEM Speckle Filtering Algorithm based on Iterative Gaussian Model. Remote Sensing Technology and Application, 2013, 28(2): 258-262.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2013.2.258        http://www.rsta.ac.cn/CN/Y2013/V28/I2/258

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