are even fewer in number. The spatial resolution of these systems is often on a decimeter level
and the dimensions are on the order of 10 to 20,000 by 10 to 20,000 pixels.
5
Therefore,
it is necessary to begin an investigation into the filtering performance of speckle filters on
very high-resolution PolSAR data.
A boxcar filter is the most fundamental and simplest means of speckle denoising. As with
other PolSAR speckle filters, it uses a coherence matrix or covariance matrix as the processing
objects. The underlying implementation strategy of a boxcar filter is to average all the matrix
elements within a square window arithmetically. This simple procedure can maintain the polari-
metric properties of certain pixels very well. However, it blurs the point targets, causes a mixture
of heterogeneous pixels, and degrades the spatial details.
1
A series of filters developed by Lee
et al., which are still blossoming, fill in part of these gaps. Scattering model-based speckle filter
(SMB) was launched by Lee et al.
6
SMB first of all applies Freeman and Durden decomposition
to the input PolSAR covariance matrix data, and divides all the pixels into three dominant scat-
tering categories: surface, volume, and double-bounce scattering, which serve as the initial input
data. Then all the pixels will be reclassified based on the Wishart distance model, which partially
characterizes the statistical property of each pixel. Finally, the filtering kernel that minimizes the
mean square error is applied, which is often found in the classic filters developed by Lee et al. for
single polarization SAR data. The Lee et al. improved sigma filter (LeeSig) is a revised version
of the classic one that was set forward for the single polarization SAR data.
7
To preserve the
strong point targets, a calculation of 98% was conceived by Lee et al. The calculation acts as
a preprocessing step that aims at distinguishing strong point targets from the other pixels.
This filter fixes the deficiencies of the sigma range in the classic version. When implementing
denoising, it adopts the minimum-mean-square-error kernel. Meanwhile, many significantly
related explorations and experiments were done by Lopez-Martinez et al., who stated that
the characterization of the multidimensional or multichannel speckle noise component played
a pivotal role in the processing of PolSAR data.
8–11
They established a compound model that
consists of a multidimensional, zero-mean, complex Gaussian random variable, and a random
texture variable was established. They presented a model-based filter (MB) that processes the
diagonal elements and off-diagonal elements differently.
As the core of an An-Yang filter, the pretest approach was devised by Chen et al.
12
It employs
nonlocal noise filtering in optical image processing. It uses the similarity of patches rather than
pixels to distinguish homogeneous pixels from heterogeneous pixels. The similarity between two
patches is obtained and then converted into the weight that will be assigned to the corres ponding
homogeneous pixel. Finally, with corresponding weight for the homogeneous pixels, a boxcar-
style average is carried out. Nonlocal means filter (NM), which also adopts the principle of
nonlocal noise filtering, was introduced by Zhong et al.
13
It combines the structure similarit y
introduced by the NM filter with the homogeneity similarity introduced by the LeeSig filter.
It behaves like the LeeSig filter when estimating the filtered covariance matrices. There is
a significant difference between the An-Yang filter and NM, although they both originate
from the nonlocal method. Mean shift-based algorithm (MS) was proposed by Lang et al.
14
The MS is well known and has been widely used in digital image filtering. Lang et al. proposed
an adaptive variable asymmetric bandwidth selection approach as a major improvement of the
conventional MS algorithm. It was reported by them that the newly derived generalized MS filter
was applicable to both single polarization SAR and fully polarimetric SAR data. Following the
speckle filtering principles for PolSAR data, a method that employs a nonlinear partial differ-
ential equation diffusion was proposed by Sun et al.
15
Nonlinear anisotropic diffusion is more
flexible when filtering toward the orientation of interesting features. It suggests a scheme using
edge-enhancing anisotropic diffusion and extends the conventional model to PolSAR speckle
filtering.
The topic of speckl e filtering is a core concern in the community of radar remote sensing and
will always be noteworthy. Due to limited space, it is impossible to encompass all the newly
proposed methods here. Validation and measurement for the rest of the filters deserve further
investigation in the future. The rest of this paper is organized as follows. In Sec. 2, SAR polar-
imetry is briefly introduced. An exchange of views with respect to speckle-filtering principles is
presented in Sec. 3. Both the qualitative and quantitative evaluations on very high-resolution
PolSAR data are elaborated in Sec. 4. Finally brief conclusions are drawn in Se c. 5.
Sun et al.: Comparative study on the speckle filters for the very high-resolution polarimetric synthetic. . .
Journal of Applied Remote Sensing 045014-2 Oct–Dec 2016
•
Vol. 10(4)
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