http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186731

Unsupervised assessment and pyramidal filtering of colored speckle (Contributo in atti di convegno)

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Unsupervised assessment and pyramidal filtering of colored speckle (Contributo in atti di convegno) (literal)

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Bruno Aiazzi; Luciano Alparone; Stefano Baronti (1999)
Unsupervised assessment and pyramidal filtering of colored speckle
in SPIE Remote Sensing 1999: SAR Image Analysis, Modeling, and Techniques II, Firenze, Italia, 22-24 Settembre 1999
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Proceedings EUROPTO Series SPIE Remote Sensing 1999: SAR Image Analysis, Modeling, and Techniques II (literal)

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\"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy Department of Electronic Engineering, University of Florence, Via S. Marta, 3, I-50139 Firenze, Italy \"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy (literal)

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Abstract

An unsupervised method is first proposed to assess the variance and the spatial correlation coefficients of speckle noise in SAR images. They are obtained as regression coefficients, the former of local standard deviation to local mean, the latter of local unity-lag covariance to local variance; both calculated on homogeneous areas. For this purpose, an automatic procedure has been developed, based on that homogeneous areas produce clusters of scatterpoints that are aligned along the regression line. On true SAR images, the method is capable to carefully reject textured regions, in which speckle may be not fully developed and the variance of the signal is not negligible. On simulated speckled images, an impressive accuracy is obtained. Once the noise parameters are known, adaptive filtering is applied in a multiresolution fashion, to take advantage of increasing SNR of the noisy image at increasing scales, as well as to cope with the spatial correlation of the noise that is halved together with the resolution. Laplacian pyramids are generalized to the noise model by defining ratios of combinations of lowpass image versions, in which the dependence of the noise on the signal is largely removed, together with the nonstationarity of the mean. Experiments on both real and synthetic images demonstrate a high accuracy of results, both for noise estimation and for filtering. (literal)

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