A review of speckle filtering in the context of estimation theory

By Ridha Touzi. IEEE Transactions on Geoscience and Remote Sensing, 2002

412 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ecology

View Paper: https://www.semanticscholar.org/paper/05aaf0db3644545ee6a29e29fff9857468f49759

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Citations in the corpus

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “In [20], several multiplicative models of speckle are described and classified according to the autocorrelations of the imaged scene and of the noise term.”
A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR (2011, background): “Indeed, most speckle filtering techniques aimed at improving radiometric data quality while preserving image details are based on a space adaptive filter [24].”
Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation (2006, methodology): “The review and the experimental results presented on intensity images in [11] encourage the use of the first approach with multiresolution techniques for optimal parameter estimation.”
On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering (2006, methodology): “Many filters have been developed to cope with speckle, with differences lying in the assumptions about the speckle model [1], being the multiplicative model the one mostly used, and paving the ground for filters such as the ones described by Lee [2], Frost et al. [3], and Kuan et al. [4], [5].”
A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction (2004, methodology): “The multiplicative model of Equation (66) was used under the assumption that speckle noise (Touzi, 2002) is stationary and completely polarized.”
Improved Sigma Filter for Speckle Filtering of SAR Imagery (2009, background): “speckle reduction for distributed scatterers requires averaging pixels within a homogeneous area [2], [3], [15], [17].”
NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, background): “Touzi [6] proposed a structural-multiresolution framework to handle both stationary and nonstationary signals and improved the previous”