A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data

Research output: Contribution to journal › Article › Research › peer review

Authors

  • Masoud Mahdianpari
  • Mahdi Motagh
  • Vahid Akbari
  • Fariba Mohammadimanesh
  • Bahram Salehi

Research Organisations

External Research Organisations

  • Helmholtz Centre Potsdam - German Research Centre for Geosciences (GFZ)
  • Memorial University of Newfoundland
  • Norwegian Institute of Bioeconomy Research
  • State University of New York (SUNY)
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Details

Original languageEnglish
Pages (from-to)64-78
Number of pages15
JournalAdvances in Space Research
Volume64
Issue number1
Early online date19 Mar 2019
Publication statusPublished - 1 Jul 2019

Abstract

Synthetic Aperture Radar (SAR) data have gained interest for a variety of remote sensing applications, given the capability of SAR sensors to operate independent of solar radiation and day/night conditions. However, the radiometric quality of SAR images is hindered by speckle noise, which affects further image processing and interpretation. As such, speckle reduction is a crucial pre-processing step in many remote sensing studies based on SAR imagery. This study proposes a new adaptive de-speckling method based on a Gaussian Markov Random Field (GMRF) model. The proposed method integrates both pixel-wised and contextual information using a weighted summation technique. As a by-product of the proposed method, a de-speckled pseudo-span image, which is obtained from the least-squares analysis of the de-speckled multi-polarization channels, is also produced. Experimental results from the medium resolution, fully polarimetric L-band ALOS PALSAR data demonstrate the effectiveness of the proposed algorithm compared to other well-known de-speckling approaches. The de-speckled images produced by the proposed method maintainthe mean value of the original image in homogenous areas, while preserving the edges of features in heterogeneous regions. In particular, the equivalent number of look (ENL) achieved using the proposed method improves by about 15% and 47% compared to the NL-SAR and SARBM3D de-speckling approaches, respectively. Other evaluation indices, such as the mean and variance of the ratio image also reveal the superiority of the proposed method relative to other de-speckling approaches examined in this study.

Keywords

    ALOS PALSAR, Contextual analysis, De-speckling, Gaussian, Markov random field (MRF), Synthetic Aperture Radar (SAR)

ASJC Scopus subject areas

Cite this

A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data. / Mahdianpari, Masoud; Motagh, Mahdi; Akbari, Vahid et al.
In: Advances in Space Research, Vol. 64, No. 1, 01.07.2019, p. 64-78.

Research output: Contribution to journal › Article › Research › peer review

Mahdianpari, M, Motagh, M, Akbari, V, Mohammadimanesh, F & Salehi, B 2019, 'A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data', Advances in Space Research, vol. 64, no. 1, pp. 64-78. https://doi.org/10.1016/j.asr.2019.03.013
Mahdianpari M, Motagh M, Akbari V, Mohammadimanesh F, Salehi B. A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data. Advances in Space Research. 2019 Jul 1;64(1):64-78. Epub 2019 Mar 19. doi: 10.1016/j.asr.2019.03.013
Mahdianpari, Masoud ; Motagh, Mahdi ; Akbari, Vahid et al. / A Gaussian random field model for de-speckling of multi-polarized Synthetic Aperture Radar data. In: Advances in Space Research. 2019 ; Vol. 64, No. 1. pp. 64-78.
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AU - Motagh, Mahdi

AU - Akbari, Vahid

AU - Mohammadimanesh, Fariba

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