Despeckling of Synthetic Aperture Radar Images Using Monte Carlo Texture Likelihood Sampling

Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a Fishe...

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Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 52; no. 2; pp. 1238 - 1248
Main Authors Glaister, Jeffrey, Wong, Alexander, Clausi, David A.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.02.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Applied geophysics
Classification algorithms
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fisher-Tippett noise
Image segmentation
Internal geophysics
Monte Carlo methods
Noise
Noise measurement
noise reduction
Radar
Radar polarimetry
Sampling
Speckle
speckle noise
Surface layer
Synthetic aperture radar
synthetic aperture radar (SAR)
Testing
Texture
Visualization
algorithms
models
speckle noise
textures
Monte Carlo analysis
sampling
Fisher-Tippett noise
image quality
synthetic aperture radar (SAR)
signal-to-noise ratio
noise
noise reduction
reduction
strategy
performances
Synthetic aperture radar
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ISSN0196-2892
1558-0644
DOI10.1109/TGRS.2013.2248739

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Summary:Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a Fisher-Tippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio, and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2013.2248739