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Journal Articles Journal of Mathematical Imaging and Vision Year : 2022

Speckle reduction in matrix-log domain for synthetic aperture radar imaging

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Abstract

Synthetic aperture radar (SAR) images are widely used for Earth observation to complement optical imaging. By combining information on the polarization and the phase shift of the radar echos, SAR images offer high sensitivity to the geometry and materials that compose a scene. This information richness comes with a drawback inherent to all coherent imaging modalities: a strong signal-dependent noise called "speckle". This paper addresses the mathematical issues of performing speckle reduction in a transformed domain: the matrix-log domain. Rather than directly estimating noiseless covariance matrices, recasting the denoising problem in terms of the matrix-log of the covariance matrices stabilizes noise fluctuations and makes it possible to apply off-the-shelf denoising algorithms. We refine the method MuLoG by replacing heuristic procedures with exact expressions and improving the estimation strategy. This corrects a bias of the original method and should facilitate and encourage the adaptation of general-purpose processing methods to SAR imaging.
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Dates and versions

ujm-03275572 , version 1 (01-07-2021)

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Charles-Alban A Deledalle, Loïc Denis, Florence Tupin. Speckle reduction in matrix-log domain for synthetic aperture radar imaging. Journal of Mathematical Imaging and Vision, 2022, 64, pp.298-320. ⟨10.1007/s10851-022-01067-1⟩. ⟨ujm-03275572⟩
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