极化合成孔径雷达相干斑抑制极化信息保持能力定量评估

针对采用极化特征图主观评估PolSAR相干斑抑制算法的极化信息保持能力存在一定的不足,提出了一种基于极化特征图相关系数的相干斑抑制效果评估方法.该方法实现了对PolSAR相干斑抑制算法极化信息保持能力的定量评估,能够更为精确地反映不同滤波器及滤波参数变化对PolSAR散射特性的影响.仿真数据和实测ESAR数据的相干斑抑制效果评估实验,验证了该方法的有效性.     

Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method

An ultrasound speckle reduction method is proposed in this paper. The filter, which enhances the power of anisotropic diffusion with the Smallest Univalue Segment Assimilating Nucleus (SUSAN) edge detector, is referred to as the SUSAN-controlled anisotropic diffusion (SUSAN_AD). The SUSAN edge detector finds image features by using local information from a pseudo-global perspective. Thanks to the noise insensitivity and structure preservation properties of SUSAN, a better control can be provided to the subsequent diffusion process. To enhance the adaptability of the SUSAN_AD, the parameters of the SUSAN edge detector are calculated based on the statistics of a fully formed speckle (FFS) region. Different FFS estimation schemes are proposed for envelope-detected speckle images and log-compressed ultrasonic images. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are employed to enhance the robustness of the method. Both synthetic and real ultrasound images are used to evaluate the proposed method. The performance of the SUSAN_AD is compared with four other existing speckle reduction methods. It is shown that the proposed method is superior to other methods in both noise reduction and detail preservation.     

Adaptive noise-reducing anisotropic diffusion filter

In image processing and computer vision, the denoising process is an important step before several processing tasks. This paper presents a new adaptive noise-reducing anisotropic diffusion (ANRAD) method to improve the image quality, which can be considered as a modified version of a speckle-reducing anisotropic diffusion (SRAD) filter. The SRAD works very well for monochrome images with speckle noise. However, in the case of images corrupted with other types of noise, it cannot provide optimal image quality due to the inaccurate noise model. The ANRAD method introduces an automatic RGB noise model estimator in a partial differential equation system similar to the SRAD diffusion, which estimates at each iteration an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of pre-segment image variances. Compared to the conventional SRAD filter, the proposed filter has the advantage of being adapted to the color noise produced by today’s CCD digital camera. The simulation results show that the ANRAD filter can reduce the noise while preserving image edges and fine details very well. Also, it is favorably compared to the fast non-local means filter, showing an improvement in the quality of the restored image. A quantitative comparison measure is given by the parameters like the mean structural similarity index and the peak signal-to-noise ratio.     

基于偏微分方程的医学超声图像去噪方法

研究了各向异性扩散方程在医学超声图像去噪中的应用。在理论上对去噪原理进行了分析,并在此基础上采用改进的针对乘性噪声的各向异性扩散算法对医学超声图像去噪,实验结果表明,该方法在有效去除噪声的同时较好地保留了医学超声图像中的重要细节信息,使图像的细节部分清晰。该方法可以有效地去除超声图像斑纹噪声,提高图像的质量。     

引入欧氏距离的各向异性扩散相干斑抑制

目的 相干斑噪声严重影响SAR影像解译。抑制相干斑同时,获取较好的边缘保持效果始终是相干斑抑制的重点。针对该问题,提出一种引入欧氏距离的各向异性扩散（EDAD）相干斑抑制方法。方法 EDAD算法以P-M模型与SRAD算法为基础,利用邻近像素间区域欧氏距离代替原有边缘检测算子,自适应区分同质区与异质区,有效构造各向异性扩散系数,完成相干斑抑制。结果 运用EDAD算法与现存各向异性扩散算法对截取的两景TanDEM-X影像进行试验研究并比较各类算法的评估参数。EDAD算法的等效视数分别为3.996与5.859,均高于其他算法,体现优越的相干斑抑制能力;EDAD算法相干斑抑制前后比值影像的均值分别为0.999与1.001,方差分别为0.270与0.269,较其他算法均更接近理想值1与0.273,展现更优边缘保持与相干斑抑制能力。结论 本文算法可有效提高边缘检测能力,获取更优相干斑抑制效果。经验证,对分布较散的弱相干斑区域与分布较集中的强相干斑区域均有较好适用性。     

基于惩罚系数自适应修正的SAR图像滤波新算法

合成孔径雷达(SAR)图像存在较强的相干斑点噪声,严重地影响了地物信息的提取与SAR图像的应用效果。提出了一种新的SAR图像斑点噪声滤波算法,该算法以一种基于膜模型的M arkov随机场的近似最优迭代滤波算法(TSPR)为基础,考虑了邻域空间关系对势能函数的影响,并通过在迭代过程中自适应修正惩罚系数,来达到更好的斑点噪声滤波效果。通过对含不同强度斑点噪声的退化图像的对比试验结果来看,该算法在提高处理后图像的信噪比方面,能够取得较TSPR算法更佳的效果。     

结合全局概率密度差异与局部灰度拟合的超声图像分割

由于超声图像具有高噪声、低对比度、边缘模糊不清等特点, 超声图像的分割成为图像处理领域中一个难度较高、亟待解决的问题. 本文提出了一种结合全局概率密度差异与局部灰度拟合的主动轮廓模型对超声图像进行分割的方法. 该方法分别在原始超声图像与预处理图像上利用了图像的全局和局部信息. 在原始图像上, 利用各区域的灰度分布, 并结合超声图像的背景知识对图像的全局信息建模. 为了考虑图像的局部信息, 首先对图像进行预处理, 在预处理图像上, 利用局部灰度拟合模型对图像中的局部信息进行建模. 通过分别在不同图像上对全局和局部信息建模的方式, 本方法将利用Speckle噪声与去除Speckle噪声的分割思想结合在一起. 本文提出的方法分别在模拟和临床超声图像上进行了实验. 实验结果证明, 该方法对图像中的噪声具有较好的适应性, 并对初始条件不敏感, 可以准确地对超声图像进行分割.     

改进的P-M扩散方程在相干斑抑制中的应用

直接基于Perona-Malik扩散方程的滤波算法对于加性噪声非常有效,但是对于乘性噪声（如合成孔径雷达（SAR）图像相干斑噪声）收效甚微。提出了一种基于改进的Perona-Malik扩散方程抑制SAR图像相干斑噪声的新算法。分析对数变化对相干斑噪声的影响,为将P-M扩散方程应用于相干斑噪声抑制奠定了理论基础;通过P-M扩散和稳健统计学的联系,建立了基于Biweight Estimator误差模型的扩散系数;同时利用非线性衰减技术对梯度阈值的选择改进。实验表明,该方法不仅有效抑制了SAR图像相干斑噪声,较好地保持了细节和边缘信息,而且视觉效果比较好。     

A diffusion stick method for speckle suppression in ultrasonic images

This paper presents a diffusion stick method for speckle suppression in ultrasonic images. An asymmetric stick filter kernel is firstly defined by decomposing the rectangle neighborhood into line segments of variable orientations. Then, the weighted sum of averages along each stick is used to produce the filtered images. Implemented in an iteration scheme, our method works as a pseudo-diffusion process, where the diffusivity is controlled by a normalized variance function. Experiments of synthetic and real images show that the diffusion stick technique performs effectively in suppressing speckle noise, preserving resolvable structures and enhancing linear features.     

A novel super-resolution method of PolSAR images based on target decomposition and polarimetric spatial correlation

The polarimetric synthetic aperture radar (PolSAR) is becoming more and more popular in remote-sensing research areas. However, due to system limitations, such as bandwidth of the signal and the physical dimension of antennas, the resolution of PolSAR images cannot be compared with those of optical remote-sensing images. Super-resolution processing of PolSAR images is usually desired for PolSAR image applications, such as image interpretation and target detection. Usually, in a PolSAR image, each resolution contains several different scattering mechanisms. If these mechanisms can be allocated to different parts within one resolution cell, details of the images can be enhanced, which that means the resolution of the images is improved. In this article, a novel super-resolution algorithm for PolSAR images is proposed, in which polarimetric target decomposition and polarimetric spatial correlation are both taken into consideration. The super-resolution method, based on polarimetric spatial correlation (SRPSC), can make full use of the polarimetric spatial correlation to allocate different scattering mechanisms of PolSAR images. The advantage of SRPSC is that the phase information can be preserved in the processed PolSAR images. The proposed methods are demonstrated with the German Aerospace Center (DLR) Experimental SAR (E-SAR) L-band full polarized images of the Oberpfaffenhofen Test Site Area in Germany, obtained on 30 September 2000. The experimental results of the SRPSC confirms the effectiveness of the proposed methods.¹     

基于异性扩散-中值滤波的超声医学图像去噪方法

针对超声图像存在一种特殊的斑点噪声,使图像边界与细节变得模糊而严重影响图像质量的问题,提出了一种新的去除医学图像斑点噪声的方法,它利用中值滤波和各向异性扩散相结合,不仅可以有效地去除噪声而且很好地保持了边缘、局部细节信息.此外,该方法在扩散过程中,梯度阈值选取的不同对图像结果影响很小,这极大地提高了该算法的健壮性.实验中,通过和各向异性扩散、中值滤波等方法的比较,表明该方法具有良好的去噪效果.     

A PolSAR image speckle filtering method preserving point targets and dominant scattering mechanisms

ABSTRACT

A new approach in polarimetric synthetic aperture radar (PolSAR) speckle filtering is proposed in this article. The proposed method preserves both point targets and dominant scattering mechanisms. The point targets are detected based on the span image, and they are then neither filtered nor involved in the other pixels’ filtering. To achieve the protection of the dominant scattering mechanism of each pixel, only pixels of the same dominant scattering mechanism as the centre pixel are included in the selection of the homogeneous pixels. Both point targets not being filtered and fact that only pixels of the same dominant scattering mechanism are included in the selection of the homogeneous pixels, which greatly improves the filtering efficiency. A likelihood-ratio test statistic based on the PolSAR covariance matrices is applied to determine the homogeneous pixels. Finally, the speckle filtering is processed using the weighted minimum mean square error estimator on the homogeneous pixels. We demonstrate the obvious advantages of the proposed method over other algorithms in the preservation of point targets and dominant scattering mechanisms, speckle suppression, protection of detail information, and maintenance of polarization information, by the use of both simulated and real PolSAR data.     

Speckle reduction of ultrasound images based on Rayleigh-trimmed anisotropic diffusion filter

A novel method is proposed to reduce speckle in ultrasound images. Based on the assumption of Rayleigh distribution of speckle, a Rayleigh-trimmed filter is first proposed to estimate the relative standard deviations of local signals and the results are used to determine the parameter that controls an alpha-trimmed mean filter for suppressing the primary noise. Then the anisotropic diffusion is subsequently applied to further reduce noise while enhancing features and structures in the original image. We also extend the proposed method to three-dimensional space by introducing time as one additional dimension. The proposed method effectively utilizes the statistical characteristics of speckle and the two-step despeckling algorithm reduces speckle significantly while retaining important features. The effectiveness of the proposed method is well demonstrated by experiments on both simulated and real ultrasound images.     

基于各向异性扩散的图像平滑及在三维重构预处理中的应用

采用基于各向异性扩散的偏微分方程，其初始值为输入图像，转化为差分格式迭代求解滤波结果。在去除噪声的同时，保持重要的边缘和局部细节。在此基础上提出了8向的各向异性扩散和边缘增强的处理技术，取得了满意的结果，并将此切片图像经聚类分群运用到三维重构中，使重构的效果更好。     

Revisiting the effect of spatial resolution on information content based on classification results

Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kinds of images, and how it is affected by usual processing techniques. Previous works have resulted in various approaches for quantifying image information content. In this paper, we study this problem from the classification accuracy viewpoint, focusing on the filtering and the classification stages. Thus, through classified images, we verify how changing the properties of the input data affects their quality. The input is an actual PolSAR image, the control parameters are (i) the filter (Local Mean, LM, or Model-Based PolSAR, MBPolSAR) and the size of their support, and (ii) the classification method (Maximum Likelihood, ML, or Support Vector Machine, SVM), and the output is the precision of the classification algorithm applied to the filtered data. To expand the conclusions, this study deals not only with Classification Accuracy but also with Kappa and Overall Accuracy as measures of map precision. Experiments were conducted on two airborne PolSAR images. Differently from what was observed in previous works, almost all quality measures are good and increase with degradation, i.e. the filtering algorithms that we used always improve the classification results at least up to supports of size 7 × 7.     

An improved speckle-reduction algorithm for SAR images based on anisotropic diffusion

This paper mainly studies the algorithm of anisotropic diffusion for speckle noise removal of SAR images. Because the Gauss curvature driven diffusion method is sensitive to the noise and is of low efficiency on suppressing the speckle noise, an improved denoising algorithm is proposed. The new algorithm introduces the difference curvature as the diffusion coefficients of the function, which solves the problem that Gauss curvature driven diffusion is sensitive to the speckle noise, further, Tukey’s biweight function is used to control the curvature diffusion model, which can not only better protect edges, but also automatically control the diffusion. Numerical experiments show that the improved algorithm can preserve the information of textures, edges while inhibiting the speckle of SAR images.     

SRAD-CNN for adaptive synthetic aperture radar image classification

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples.     

Unsupervised classification for polarimetric SAR images based on the improved CFSFDP algorithm

In polarimetric synthetic aperture radar (PolSAR) image processing, the number of classes is an important factor for PolSAR image classification. Therefore, how to accurately estimate the number of PolSAR image classes is an important issue. In this article, we propose a novel unsupervised classification method which can accurately estimate the number of classes for PolSAR images. First, the PolSAR image is initialized into many small clusters by using the complementary information from Yamaguchi decomposition and distribution characteristics of data. Second, the improved clustering by fast search and find of density peaks, named as improved CFSFDP algorithm, is introduced to select the appropriate category number. Finally, to improve the representation of each category, the PolSAR data set is classified by an iterative fine-tuning process based on a complex K-Wishart function. The performance of the proposed classification approach is presented and analysed on three real data sets. The experimental results show that the proposed classification method can accurately estimate the category number and enhance the classification accuracy in comparison with other traditional methods. It is also shown that the data distribution characteristic has the additional information beyond the target scattering decomposition, and this information is important for the initialization.     

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Speckle reduction is a prerequisite for many image processing tasks in synthetic aperture radar images, as well as all coherent images. In recent years, predominant state-of-the-art approaches for despeckling are usually based on nonlocal methods which mainly concentrate on achieving utmost image restoration quality, with relatively low computational efficiency. Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality. To this end, we exploit a newly developed trainable nonlinear reaction diffusion (TNRD) framework which has proven a simple and effective model for various image restoration problems. In the original TNRD applications, the diffusion network is usually derived based on the direct gradient descent scheme. However, this approach will encounter some problem for the task of multiplicative noise reduction exploited in this study. To solve this problem, we employed a new architecture derived from the proximal gradient descent method. Taking into account the speckle noise statistics, the diffusion process for the despeckling task is derived. We then retrain all the model parameters in the presence of speckle noise. Finally, optimized nonlinear diffusion filtering models are obtained, which are specialized for despeckling with various noise levels. Experimental results substantiate that the trained filtering models provide comparable or even better results than state-of-the-art nonlocal approaches. Meanwhile, our proposed model merely contains convolution of linear filters with an image, which offers high-level parallelism on GPUs. As a consequence, for images of size \(512 \times 512\), our GPU implementation takes less than 0.1 s to produce state-of-the-art despeckling performance.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.