Iterative Bilateral Filtering of Polarimetric SAR Data

Abstract

Abstract-In this paper, we introduce an iterative speckle filtering method for polarimetric SAR (PolSAR) images based on the bilateral filter. To locally adapt to the spatial structure of images, this filter relies on pixel similarities in both spatial and radiometric domains. To deal with polarimetric data, we study the use of similarities based on a statistical distance called Kullback-Leibler divergence as well as two geodesic distances on Riemannian manifolds. To cope with speckle, we propose to progressively refine the result thanks to an iterative scheme. Experiments are run over synthetic and experimental data. First, simulations are generated to study the effects of filtering parameters in terms of polarimetric reconstruction error, edge preservation and smoothing of homogeneous areas. Comparison with other methods shows that our approach compares well to other state of the art methods in the extraction of polarimetric information and shows superior performance for edge restoration and noise smoothing. The filter is then applied to experimental data sets from ESAR and FSAR sensors (DLR) at L-band and S-band, respectively. These last experiments show the ability of the filter to restore structures such as buildings and roads and to preserve boundaries between regions while achieving a high amount of smoothing in homogeneous areas.

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

“…This idea can be traced-back to the early 80s with the introduction of Lee's sigma filter [23] and Yaroslavsky's filter [24], latter popularized under the name "bilateral filter" [25], [26]. Similarly to IDAN, the extension of this approach to SAR data suffers from a selection bias that can be corrected using the so-called improved sigma filter [27].…”

“…It has recently been extended following the principles of the successful BM3D approach with SAR-BM3D [41]. Different statistical tests have been proposed for the pixel selection, including: joint-likelihood criteria [37], [38], [42], generalized likelihood ratio tests [40], [43], stochastic and geodesic distances [26], [44]. Some of them are free of selection bias (see Section 3.8 in [45] for more details).…”

Geomorphology and landforms distribution in selected ice-free areas in the South Shetland Islands, Antarctic Northern Peninsula region

“…After importing the data into PolSARPro (Pottier, 2010), a bilateral polarimetric speckle filter (D'Hondt et al, 2013) was applied to allow the extraction of polarimetric features. Polarimetric features were derived from the analysis of the coherency matrix and different polarimetric parameters are extracted based on eigenvalues and eigenvectors (Lee and Pottier, 2009).…”

Feature-Based Nonlocal Polarimetric SAR Filtering

“…The relevant filtering methods [7][8][9][11][12][13][14] perform the weighted average by measuring the similarity of the statistical target information or spatial distance. The spatial distance is unsuitable for multiplicative speckle noise of the PolSAR image.…”