Interferogram phase noise filter using nonlinear anisotropic diffusion equation

被引:0
|
作者
Sun, L [1 ]
Hu, ML
机构
[1] Anhui Univ, Intelligent Comp & Signal Proc Lab, Hefei 230039, Peoples R China
[2] Anhui Univ, Sch Math & Computat Sci, Hefei 230039, Peoples R China
[3] E China Res Inst Elect Engn, Hefei 230031, Peoples R China
来源
CHINESE JOURNAL OF ELECTRONICS | 2005年 / 14卷 / 04期
关键词
Interferometric synthetic aperture radar (InSAR); noise suppressing; phase unwrapping; nonlinear partial differential equations (PDE); anisotropic diffusion;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The phase noise suppression methods applied on the interferogram of Interferometric synthetic aperture radar (InSAR) are studied. Filtering phase noise in an interferogram is an important aspect in InSAR data processing. But any improper altering of the wrapped phase may influence the quality of derived DEM because the interferometric phase contains the topographic information. Therefore, one of the difficulties in phase noise filtering is how to remove the noise and preserve the spatial resolution effectively. In this paper, a new adaptive approach based on the nonlinear anisotropic diffusion equation is presented for removing the noise in the interferogram. The key idea is that for low gradients, isotropic smoothing is performed, and for high gradient, smoothing is only applied in the direction of the isophote and not across it. During the course of noise suppressing, image features and their directions are extracted. Less smoothing is in the locations with strong image feature, and more smoothing in the locations with weak image feature; minimal smoothing in the directions across the image features, and maximal smoothing in the directions along the image features. At the end of this paper, this approach is compared with some existing approachs for InSAR noise filtering and the experimental results by processing Canada Radarsat1 data are used to confirm that the method is more effective in suppressing noise in the interferogram.
引用
收藏
页码:653 / 655
页数:3
相关论文
共 50 条
  • [21] On the use of nonlinear anisotropic diffusion filters for seismic imaging using the full waveform
    Metivier, L.
    Brossier, R.
    INVERSE PROBLEMS, 2022, 38 (11)
  • [22] Adaptive noise reduction of InSAR data based on anisotropic diffusion models and their applications to phase unwrapping
    Wang, C
    Gao, X
    Zhang, H
    WAVE PROPAGATION, SCATTERING AND EMISSION IN COMPLEX MEDIA, 2004, : 63 - 72
  • [23] A novel 3D anisotropic diffusion filter
    Pop, Sorin
    Terebes, Romulus
    Da Costa, Jean-Pierre
    Germain, Christian
    Borda, Monica
    Ludusan, Cosmin
    Lavialle, Olivier
    THREE-DIMENSIONAL IMAGE PROCESSING (3DIP) AND APPLICATIONS, 2010, 7526
  • [24] Anisotropic Diffusion Based Multiplicative Speckle Noise Removal
    Gao, Mei
    Kang, Baosheng
    Feng, Xiangchu
    Zhang, Wei
    Zhang, Wenjuan
    SENSORS, 2019, 19 (14)
  • [25] Noise-Driven Anisotropic Diffusion Filtering of MRI
    Krissian, Karl
    Aja-Fernandez, Santiago
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009, 18 (10) : 2265 - 2274
  • [26] Anisotropic diffusion for noise removal of band pass signals
    Mahmoodi, Sasan
    SIGNAL PROCESSING, 2011, 91 (05) : 1298 - 1307
  • [27] Dictionary-based anisotropic diffusion for noise reduction
    Cho, Sung In
    Kang, Suk-Ju
    Kim, Hi-Seok
    Kim, Young Hwan
    PATTERN RECOGNITION LETTERS, 2014, 46 : 36 - 45
  • [28] Robust anisotropic diffusion scheme for image noise removal
    Barbu, Tudor
    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014, 2014, 35 : 522 - 530
  • [29] First order hyperbolic approach for Anisotropic Diffusion equation
    Chamarthi, Amareshwara Sainadh
    Nishikawa, Hiroaki
    Komurasaki, Kimiya
    JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 396 : 243 - 263
  • [30] Improved Geometric Anisotropic Diffusion Filter for Radiography Image Enhancement
    Ben Gharsallah, Mohamed
    Ben Mhammed, Issam
    Ben Braiek, Ezzedine
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2018, 24 (02) : 231 - 239