Paper Info
Title
Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means
Abstract
This paper presents a technique for reducing speckle in Polarimetric Synthetic Aperture Radar (PolSAR) imagery using nonlocal means and a statistical test based on stochastic divergences. The main objective is to select homogeneous pixels in the filtering area through statistical tests between distributions. This proposal uses the complex Wishart model to describe PolSAR data, but the technique can be extended to other models. The weights of the location-variant linear filter are function of the p-values of tests which verify the hypothesis that two samples come from the same distribution and, therefore, can be used to compute a local mean. The test stems from the family of (h-@f) divergences which originated in Information Theory. This novel technique was compared with the Boxcar, Refined Lee and IDAN filters. Image quality assessment methods on simulated and real data are employed to validate the performance of this approach. We show that the proposed filter also enhances the polarimetric entropy and preserves the scattering information of the targets.
Year
DOI
Venue
2014
10.1016/j.patcog.2013.04.001
Pattern Recognition
Keywords
DocType
Volume
location-variant linear filter,information theory,refined lee,polarimetric sar imagery,speckle reduction,polsar data,idan filter,stochastic distance,novel technique,statistical test,nonlocal mean,proposed filter,polarimetric synthetic aperture radar,synthetic aperture radar,multiplicative noise,hypothesis testing
Journal
47
Issue
ISSN
Citations 
1
0031-3203
32
PageRank 
References 
Authors
1.20
31
4
Name
Order
Citations
PageRank
Leonardo Torres1524.44
Sidnei J. S. Sant'Anna2397.42
Corina da Costa Freitas318319.86
Alejandro C. Frery436838.29