10 December 1999 Unsupervised assessment and pyramidal filtering of colored speckle
Author Affiliations +
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 using 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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bruno Aiazzi, Bruno Aiazzi, Luciano Alparone, Luciano Alparone, Stefano Baronti, Stefano Baronti, } "Unsupervised assessment and pyramidal filtering of colored speckle", Proc. SPIE 3869, SAR Image Analysis, Modeling, and Techniques II, (10 December 1999); doi: 10.1117/12.373155; https://doi.org/10.1117/12.373155
PROCEEDINGS
12 PAGES


SHARE
Back to Top