改进极化白化滤波的边缘检测

针对SAR影像边缘检测受斑点噪声影响严重和极化信息利用不充分的问题,用滑动模板边缘两侧目标的协方差矩阵代替了极化白化滤波中杂波背景与窗口中心的协方差矩阵,提出一种基于改进极化白化滤波的边缘检测新方法,充分利用了极化通道间的相关性,在有效抑制斑点噪声的同时,提高了极化信息的利用率。模拟和真实极化影像的实验验证了新方法的有效性。     

极化SAR相干斑抑制的非局部加权最小均方误差滤波算法

目的 相干斑的存在严重影响了极化合成孔径雷达(PolSAR)的影像质量.对相干斑的抑制是使用SAR数据的必不可少的预处理程序.提出一种基于非局部加权的线性最小均方误差(LMMSE)滤波器的极化SAR滤波的方法.方法 该方法的主要过程是利用非局部均值的理论来获取LMMSE估计器中像素样本的权重.同时,在样本像素的选取过程中,利用待处理像素的极化散射特性和邻域块的异质性来排除不相似像素以加速算法,同时达到保持点目标和自适应调节块窗口大小的目的.结果 模拟影像和真实影像上进行的实验结果表明,采用这种方法滤波后影像的质量得到明显改善.和传统的LMMSE算法相比,无论是单视的影像还是多视的影像,本文方法去噪结果的等效视数都高出8视以上;峰值信噪比也提升了5.8 dB.同时,去噪后影像分类的总体精度也达到了83％以上,该方法的运行效率也比非局部均值算法有了较大提升.结论 本文方法不仅能够有效抑制相干斑噪声,还能较好地保持边缘和细节信息以及极化散射特性.这将会为后续高效利用SAR数据提供保障.     

改进的基于非局部均值的极化SAR相干斑抑制

贝叶斯形式的非局部均值模型在极化SAR图像相干斑抑制中有良好的应用,在实现抑制相干斑的同时较好地保持了边缘细节和点目标.通过分析合成孔径雷达(SAR)图像多视数据的空间统计分布,结合贝叶斯形式的非局部均值模型,得出在该模型下多视与单视SAR图像中像素间相似性度量函数一致性的结论,并对该相似性度量函数进行了修正,使之满足对称性;最后针对算法全局使用一个固定滤波参数影响滤波效果的问题,提出一种根据像素间相似程度自适应选取滤波参数的方法.实验结果验证了本文算法的有效性.     

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.     

基于独立分量分析的极化SAR图像的相干斑抑制

研究基于独立分量分析( ICA)的极化合成孔径雷达（SAR）图像相干斑抑制方法。该方法将极化SAR图像斑点噪声的乘积模型,变换为应用ICA的信号独立加噪模型。并且将HV/VV的比值图像,也作为ICA的输入数据。利用ICA 的分离性,得到了分别对应于HH、HV和VV极化的三幅降噪图像。经本文方法处理后的图像,其相干斑噪声得到了有效的抑制,具有较高的等效视数,明显地改善了图像的质量。     

Study on segmentation and interpretation of multi-look polarimetric SAR images

The segmentation and interpretation of multi-look polarimetric synthetic aperture radar (SAR) images is studied. We first introduce a multi-look polarimetric whitening filter (MPWF) to reduce the speckle in multi-look polarimetric SAR images. Then, by utilizing the wavelet multiresolution approach to extract the texture information in different scales and the Markov random field (MRF) model to characterize the spatial constraints between pixels in each scale level, a multiresolution segmentation algorithm (MSA) to segment the speckle-reduced SAR images is presented. The MSA first segments the image at the lowest resolution level and then proceeds to progressively higher resolutions until individual pixels are well classified. An unsupervised step to estimate both the optimal number of texture classes and their model parameters is also included in the MSA so that the segmentation can be implemented without supervision. Finally, in order to interpret the results of the unsupervised segmentation and to understand the whole polarimetric SAR image, we develop an image interpretation approach which jointly utilizes the scattering mechanism identification and target decomposition approaches. Experimental results with the real-world multi-look polarimetric SAR image demonstrate the effectiveness of the segmentation and interpretation approaches.     

The influence of filtration and decomposition window size on the threshold value and accuracy of land-cover classification of polarimetric SAR images

In this study we use ALOS PALSAR satellite data to classify land cover using a decision tree algorithm. We apply polarimetric decomposition methods to coherence and covariance matrices obtained from the data and then use threshold values to classify terrain. We evaluate the influence of speckle filter and decomposition window sizes on the threshold value used in the decision algorithm and on the accuracy of the classification. We also study the sensitivity of the classification to the accuracy of the threshold value.

First, we processed a fully polarimetric Synthetic Aperture Radar (SAR) L-band image using different sizes of speckle filtration and decomposition window (3 × 3 pixels, 5 × 5, 7 × 7, 9 × 9), and the decomposition methods available in PolSARPro software. We evaluated these methods and chose the most efficient. Then we developed a simple hierarchical classification scheme based on threshold values. In the first step we divided the terrain into smooth and rough areas and then separated these into more detailed subclasses (water and agriculture, and forest and urban) which correspond to smooth and rough areas, respectively. A more detailed analysis separated continuous and discontinuous urban fabric and deciduous and coniferous forests. The maximum overall accuracy of the classification was 86.1% for the four main land cover classes, and 80.4% for the six more detailed classes. The accuracy of the classification dropped by about 10% when non-optimal window sizes were used in image filtration or decomposition.     

极化SAR图像相干斑抑制的ICA方法与分析

极化合成孔径雷达(synthetic aperture radar,SAR)图像为雷达图像中的信息处理和获取提供了更为便捷的途径。提出了基于独立分量分析(independent component analysis,ICA)的极化SAR图像相干斑抑制方法。该方法将极化SAR图像斑点噪声的乘积模型,变换为应用ICA的信号加噪模型。并且将HV/VV的比值图像,也作为ICA的输入数据。分别使用几种不同的ICA算法,得到了分别对应于HH、HV和VV极化的3幅降噪图像,并对结果进行了比较分析。实验结果表明,应用ICA算法可以有效地降低极化SAR图像的相干斑噪声,提高图像质量。     

多极化SAR图像斑点抑制综述

SAR(合成孔径雷达)图像斑点抑制一直是SAR图像应用中的一个重要课题。近二十几年来,伴随着SAR的发展和应用,涌现出了大量的斑点抑制算法。这些算法可分为两类:一类是基于单通道数据;另一类是基于多通道数据。对相干斑抑制问题进行了全面系统的研究,并侧重于多极化SAR图像的斑点抑制,归纳总结了近十几年来国内外所出现的多极化SAR图像斑点抑制算法,并对各种算法的性能进行了分析和比较。     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

Polarimetric SAR statistical analysis using alpha-stable distribution and its application in optimal despeckling

In this article, the statistical model of the polarimetric synthetic aperture radar (SAR) single-look complex image is analysed using alpha-stable distribution. It is better to use alpha-stable distribution than Gaussian distribution to represent the statistical characteristics of the polarimetric SAR image. A polarimetric SAR covariance matrix estimation method based on fractional lower-order statistics (FLOS) is proposed. Based on this model, an adaptive polarimetric SAR optimal despeckling method based on FLOS is developed. This algorithm adaptively estimates the characteristic exponents of each channel and uses these estimated alphas to calculate the parameters for the optimal despeckling adaptively. The experiments using polarimetric SAR data demonstrate that the proposed method not only reduces the blurs that occur in the area of impulsive reflectors in the result of the original optimal despeckling method, but also maintains the speckle reduction ability (equivalent number of looks).     

基于ICA和SNF的SAR机场目标提取

针对合成孔径雷达(SAR)影像相干斑噪声强烈且分布形式及参数获取困难的问题,提出一种基于独立分量分析(ICA)和序列非线性滤波(SNF)实现多极化SAR影像相干斑噪声抑制和机场目标快速提取方法。利用ICA从多极化SAR影像中自动分离出图像数据与相干斑噪声,自动选择相干斑指数最小的分量为图像分量。通过SNF从分离出的图像分量中提取出机场目标。采用ENVISAT ASAR多极化影像进行实验,结果表明该方法能快速准确地提取多极化SAR影像中的机场目标。     

Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means

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  –?$?$) 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.     

A study of land cover classification using polarimetric SAR parameters

Classification of the Earth's surface constitutes an important application of polarimetric synthetic aperture radar (SAR) data; in turn, it may represent an efficient way for investigating their different representations. The polarimetric parameters most frequently taken into account for classification have been the incoherent ones. A similar use of coherent methods appears to have been scarcely considered and remained neglected. In this contribution, we wish to address this issue, testing and comparing a wide range of polarimetric SAR parameters, coherent and incoherent. Another original aspect of this work is the study of the dependence of the classification results on the varying size of averaging windows of pixels. Such an analysis will permit us to evaluate the importance of speckle reduction and to prove if the chosen polarimetric parameters describe only point‐like physical properties of the targets or if they also contain ‘extended’, local information. The goal is to provide an objective estimate of the quality of the classification of polarimetric parameters and afford their comparison, an exercise hitherto unavailable in the literature in common knowledge.     

Applications of ICA for the enhancement and classification of polarimetric SAR images

Independent components analysis (ICA) based methods for polarimetric synthetic aperture radar (SAR) image speckle reduction and ground object classification are studied. Several independent components can be extracted from polarimetric SAR images using ICA directly. The component with lowest speckle index is regarded as the scene after speckle reduction. The disadvantage of this method is that only one image is kept and most polarization information will be lost. In this paper, we use ICA‐sparse‐coding shrinkage (ICA‐SPS) based speckle reduction method, which is implemented on each individual image and can keep polarization information. It is carried out on the combined channels obtained by Pauli‐decomposition rather than original polarization channels in order to keep relative phase information among polarization channels and get better performance. After ICA‐SPS, the effect of speckle suppression on SAR image classification can be compared favourably with other methods by combining the channels into a false colour image. At last, a new ICA‐based classification method is presented. In this method, four independent components are separated by ICA from five polarization and combined channels. One of these independent components which includes little ground object information is regarded as speckle noise and therefore be discarded. The remaining three components can be treated as subordination coefficients of three kinds of targets. A classified image can be obtained based on the components. And by composing these three channels in RGB colour pattern, a false colour image can be constructed.     

盲信号分离和序贯滤波的SAR影像水体提取

采用一种新的基于盲信号分离（BSS）和序列非线性滤波方法实现多极化合成孔径雷达（SAR）影像相干斑噪声抑制和水体目标快速提取。SAR影像具有强烈乘性相干斑噪声,影像数据为非高斯分布,但其具体分布形式及参数难以获得。利用基于独立分量分析的盲信号分离方法,不需要知道SAR影像的具体分布,通过对数量化将相干斑噪声转化为与图像数据相互独立的加性噪声,从多极化SAR影像中自动分离出图像数据与相干斑噪声,并自动选择相干斑指数最小的分量为图像分量。针对SAR影像水体目标的亮度及形状分布特征,进一步采用序列非线性滤波处理,从分离出的图像分量中提取出水体目标。利用ENVISAT ASAR多极化影像进行了实验,结果表明该方法可以快速准确地提取多极化SAR影像中的水体目标。     

Similarity-enhanced target detection algorithm using polarimetric SAR images

Target detection and analysis using polarimetric synthetic aperture radar (PolSAR) images are currently of great interest in synthetic aperture radar (SAR) applications. For a complex target, the scattering characteristics are determined by different independent sub-scatterers and their interaction; therefore, the scattering characteristics should be described by a statistical method due to randomness and depolarization. Furthermore, the inherent speckle in SAR data must be reduced by spatial averaging at the expense of loss of spatial resolution. The polarimetric similarity parameter (PSP) is an effective parameter to analyse target characteristics. In order to describe a complex distributed target, two new methods for calculating PSP are proposed, namely Stokes matrix-based PSP (S-PSP) and multiple PolSAR similarity parameter (MPSP). The characteristics of a target can be described and extracted on the basis of the polarimetric similarity, and then the similarity-enhanced target detection methods using S-PSP and MPSP are implemented and demonstrated with German Aerospace Centre (DLR) experimental SAR L-band multiple temporal PolSAR images of Oberpfaffenhofen test site (DE), Germany. The results confirmed that the proposed methods are effective for detection and analysis of buildings in urban areas.     

Improved airborne PolSAR calibration algorithm based on time-variant attitude compensation

The polarimetric synthetic aperture radar (PolSAR) usually has to be calibrated before practical application, so as to compensate for polarimetric distortion. The varying platform attitude is one of the factors causing distortion but has rarely been considered in existing polarimetric calibration algorithms. With the resolution of PolSAR systems improving and the synthetic aperture time prolonging, this factor cannot simply be ignored. The varying attitude will distort the polarimetric information by rotating the polarimetric orientation angle, and such distortion changes with azimuth time. In this article, we modified the conventional polarimetric system model to take account of the time-variant impact of the unstable platform attitude. A calibration algorithm is proposed to compensate the time-variant attitude impact on the raw return data. The proposed calibration algorithm is tested on the data collected by Institute of Electronics, Chinese Academy of Sciences P-band PolSAR system. Results show that it can achieve better performance by reducing crosstalk error than two conventional methods.     

An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.     

一种基于小波域隐马尔可夫模型的SAR相干斑抑制算法

相干斑噪声是SAR图像的固有特点。对相干斑抑制的要求是在平滑噪声的同时，尽量保持原始图像的结构信息。现有的许多相干斑抑制方法各有优点和不足，没有普遍的适用性。基于图像在小波域的隐马尔可夫模型(HMMs)结构，结合SAR图像中相干斑噪声的统计特性，本文提出了一种新的小波域相干斑抑制方法。仿真及实测数据处理结果表明，该方法在有效抑制相干斑的同时，更好地保持了边缘结构。与小波域软阈值去噪方法和Lee滤波器相比较，该方法在噪声平滑及边缘保持上都取得了较大的改进，并得到了较好的视觉效果。