• DocumentCode
    2981754
  • Title

    A multiscale kernel approach to speckle suppression for SAR images

  • Author

    Xiong, Wei ; Cao, Lanying ; Jiang, Jun

  • Author_Institution
    Radar & Avionics Inst. of AVIC, Wuxi, China
  • fYear
    2009
  • fDate
    26-30 Oct. 2009
  • Firstpage
    1084
  • Lastpage
    1087
  • Abstract
    Coherent speckle noise of SAR images decreases the image quality seriously, and makes the image difficult to be understood. The recently developed multiscale kernel (MSK) is a truly mesh-free method and can handle unstructured data with noise in any dimension. In this paper, a new speckle reduction method for SAR images based on MSK is presented. Firstly, the feasibility and rationality of filtering method based on MSK is proved by the analysis of decomposition and experiment for one-dimension signal denoising. Then the SAR image is regarded as two-dimension continuous signal and is multiscale decomposed based on MSK. Finally, the decomposed image is filtered by a threshold strategy and a inversed operator is applied to recover the image. Experimental results show that the presented method can both suppress the speckle noise and keep the edges in SAR images, the performance is better than that of the traditional filter methods.
  • Keywords
    radar imaging; signal denoising; speckle; synthetic aperture radar; MSK; SAR images; coherent speckle noise; filtering method; mesh-free method; multiscale kernel approach; one-dimension signal denoising; speckle reduction method; speckle suppression; two-dimension continuous signal; Detectors; Filtering; Filters; Image edge detection; Kernel; Noise reduction; Radar imaging; Signal analysis; Speckle; Synthetic aperture radar; Multiscale Kernel (MSK); Speckle; Synthetic Aperture Radar (SAR); Wavelet Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Synthetic Aperture Radar, 2009. APSAR 2009. 2nd Asian-Pacific Conference on
  • Conference_Location
    Xian, Shanxi
  • Print_ISBN
    978-1-4244-2731-4
  • Electronic_ISBN
    978-1-4244-2732-1
  • Type

    conf

  • DOI
    10.1109/APSAR.2009.5374205
  • Filename
    5374205