Title | Despeckling of Synthetic Aperture Radar Images using Monte Carlo Texture Likelihood Sampling |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Glaister, J., A. Wong, and D. A. Clausi |
Journal | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Fisher-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. |
Despeckling of Synthetic Aperture Radar Images using Monte Carlo Texture Likelihood Sampling
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