Speckle Reduction using Stochastic Distances
Abstract
This paper presents a new approach for filter design based on stochastic distances and tests between distributions. A window is defined around each pixel, samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The technique is applied to intensity Synthetic Aperture Radar (SAR) data, using the Gamma model with varying number of looks allowing, thus, changes in heterogeneity. Modified Nagao-Matsuyama windows are used to define the samples. The proposal is compared with the Lee's filter which is considered a standard, using a protocol based on simulation. Among the criteria used to quantify the quality of filters, we employ the equivalent number of looks (related to the signal-to-noise ratio), line contrast, and edge preservation. Moreover, we also assessed the filters by the Universal Image Quality Index and the Pearson's correlation between edges.
Cite
@article{arxiv.1207.0704,
title = {Speckle Reduction using Stochastic Distances},
author = {Leonardo Torres and Tamer Cavalcante and Alejandro C. Frery},
journal= {arXiv preprint arXiv:1207.0704},
year = {2012}
}
Comments
Accepted for publication on the proceedings of the 17th Iberoamerican Congress on Patter Recognition (CIARP), to be published in the Lecture Notes in Computer Science series