高分辨率偏振合成孔径雷达成像散斑滤波技术对比分析

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"该研究论文主要探讨了高分辨率偏振合成孔径雷达（Very High Resolution Polarimetric Synthetic Aperture Radar, VHR PolSAR）成像中散斑滤波器的比较分析。作者包括Sheng Sun、Renfeng Liu、Changcai Yang、Huabing Zhou、Ji Zhao以及Jiayi Ma，发表在《应用遥感》(Journal of Applied Remote Sensing)期刊的第10卷第4期，文章编号为045014，发表于2016年。" 本文深入研究了用于VHR PolSAR图像的散斑滤波技术，这是雷达遥感领域中的一个重要问题，因为散斑噪声会降低图像的可解译性和分析精度。PolSAR系统通过捕获目标的偏振特性提供丰富的信息，但同时也伴随着复杂的散斑噪声，这使得图像处理和分析变得更具挑战性。文章中，作者比较了多种散斑滤波器的性能，这些滤波器可能包括但不限于Lee滤波器、Frost滤波器、Kuan滤波器、Gamma Map滤波器等。这些滤波器各有优缺点，例如，Lee滤波器在保留边缘信息方面表现良好，但可能会过度平滑某些区域；Frost滤波器则在抑制散斑的同时，可能会影响图像的纹理细节。作者通过实验和分析，评估了这些滤波器在处理VHR PolSAR图像时对信噪比（SNR）、保真度（Fidelity）和纹理保持能力的影响。他们可能还讨论了各种滤波器在不同场景和地物类型下的适用性，以及滤波效果对后续特征提取和目标识别的影响。此外，论文可能还涉及了滤波器的优化和改进方法，以适应VHR PolSAR图像的特性，例如，可能探讨了自适应滤波策略，这些策略可以根据局部图像特性动态调整滤波参数，从而达到更好的去噪效果。这项研究对于理解并选择适合特定应用的散斑滤波器具有重要意义，对于提高VHR PolSAR图像的分析质量和效率具有实际价值。对于从事雷达遥感、图像处理和地理信息系统领域的研究人员，以及需要处理PolSAR图像的工程师来说，这篇文章提供了宝贵的理论依据和技术参考。

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.

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.

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.

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.

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.

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.

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.

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.

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

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Vol. 10(4)

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