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

TitleDespeckling of Synthetic Aperture Radar Images using Monte Carlo Texture Likelihood Sampling
Publication TypeJournal Article
Year of Publication2014
AuthorsGlaister, J., A. Wong, and D. A. Clausi
JournalIEEE Transactions on Geoscience and Remote Sensing
KeywordsFisher-Tippett noise, noise reduction, speckle noise, synthetic aperture radar
Abstract

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 FisherTippett 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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