Non-stationary speckle reduction in high resolution SAR images

Zhihuo Xu, Quan Shi, Yunjin Chen*, Wensen Feng, Yeqin Shao, Ling Sun, Xinming Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper attempts to address non-stationary speckle reduction in high-resolution synthetic aperture radar (HR-SAR) images, using a novel Bayesian approach. First, non-stationary speckle is defined. Second, an innovative log-normal mixture model (LogNMM) is proposed to model the underlying data; the data priors are represented by using Fields of Experts (FoE); and then the despeckling model is derived based on maximum a posteriori (MAP) method. The experimental results demonstrate that the proposal produces state-of-the-art despeckling performance on synthetic and real HR-SAR data, and prove that the speckle is non-stationary in the HR-SAR data of interest.

Original languageEnglish
Pages (from-to)72-82
Number of pages11
JournalDigital Signal Processing
Publication statusPublished - 1 Feb 2018


  • Field of Experts (FoE)
  • Log normal distribution mixture model (LogNMM)
  • Maximum a posteriori (MAP)
  • Speckle
  • Synthetic aperture radar (SAR)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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