极化合成孔径雷达图像相干斑抑制和分类方法综述

极化合成孔径雷达（Polarimetric synthetic aperture radar,PolSAR）是一种多参数、多通道的微波成像系统,在农林业、地质、海洋和军事等领域有着广泛的应用前景.PolSAR图像的相干斑抑制和分类是数据解译的重要环节,已经成为遥感领域的研究热点.本文综述了现有PolSAR图像的相干斑噪声抑制和分类方法并进行展望.首先,简要介绍了PolSAR系统的主要进展和应用;然后,对PolSAR图像相干斑抑制的评价指标和方法进行综述并对几种代表性方法进行了实验对比;接下来,对PolSAR图像的特征进行分析归纳,分别对有监督、无监督和半监督的PolSAR分类方法进行总结并给出了几种有监督分类方法的实验比较;最后,对PolSAR图像相干斑抑制和分类方法未来可能的研究方向进行了思考和讨论.     

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.     

极化SAR建筑物震害信息识别研究方法综述

合成孔径雷达具有全天候、全天时的对地观测优势,全极化合成孔径雷达（PolSAR）能够同时获取4种极化信息,利用PolSAR在震后进行震害评估具有及时性和准确性的优势。首先概述了PolSAR的发展状况及其在建筑物震害信息提取中的应用;其次,基于不同数据类型（多时相数据、多源数据、单时相数据）,概述了近10 a来 PolSAR数据在建筑物震害提取中的应用及其对比分析;然后,从极化分解方法、极化特征以及纹理特征3个方面对PolSAR数据的建筑物震害提取方法进行了详细阐述;最后,提出未来研究工作的设想,以期结合地理信息数据POI补充PolSAR在震害评估精度方面的不足。     

极化SAR图像滤波方法对比分析

随着遥感技术的快速发展,极化合成孔径雷达（Polarimetric Synthetic Aperture Radar,PolSAR）已成为遥感领域重要的全天时、全天候对地观测方式。但是,基于相干成像原理的PolSAR图像会受到相干斑的影响,导致精确度下降。为了提升图像质量,减少相干斑滤波影响,文章对比分析了常用的典型极化SAR图像去噪方法,介绍了各类方法的特点,以期为雷达遥感图像的应用提供参考。     

改进Notch滤波的全极化SAR数据船舶检测方法

全极化SAR数据提供了更多的地物极化散射信息,目前被广泛的应用于海上船舶检测的应用研究。本文提出改进的Notch滤波方法,实现全极化SAR数据的海上船舶检测。该方法结合目标的极化散射特性与能量双重特点,设计针对海面、方位向模糊、相干斑噪的不同滤波,消除虚警,通过SPAN能量因子降低由于散射机制相同而造成的漏检。利用RADATSAT-2全极化精细扫描数据对本文的算法进行验证,并与PWF和SPAN方法进行对比分析,实验结果表明本文提出的方法能从海面上有效检测出各种大小的船舶,同时能抑制方位向模糊、相干斑噪以及船舶的旁瓣造成的虚警。     

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.     

一种改进的基于非局部均值的极化SAR相干斑抑制算法

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地保持了边缘细节和点目标。本文通过分析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,得出了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,提出了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。     

高分辨率极化合成孔径雷达成像研究进展

合成孔径雷达（Synthetic aperture radar,SAR）是一种有效的地球遥感技术,对观测区域进行全天时、全天候的高分辨率大范围成像,在军事侦察、环境监测和地质测绘等领域有着十分广泛的应用。随着雷达技术和地球科学的发展,人们期望能够获取更多的目标特性,传统的单极化SAR已经难以满足越来越多元化的实际应用需求。极化合成孔径雷达（Polarimetric synthetic aperture radar,PolSAR）基于多个极化通道获取目标不同极化状态下的散射特性丰富了SAR图像的信息量,拓展了SAR的应用领域。从极化数据中准确地解译目标的物理特性是PolSAR应用的重要前提。本文对PolSAR的研究进展进行了总结,重点介绍了极化目标分解算法,给出了高分辨率PolSAR实测数据处理结果,并对未来研究方向进行了展望。     

基于张量空间中的均值漂移聚类的极化SAR图像分割

提出了一种基于均值漂移(Mean Shift, MS)聚类的全极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)图像无监督分割算法. 已有的工作在将MS算法应用于全PolSAR图像分割时, 仅使用每个像素点的极化总功率值作为该像素点的特征值, 没有充分利用极化协方差矩阵或者相干矩阵所包含的完整的极化散射信息. 但是如果直接利用每个像素点的极化协方差矩阵作为特征向量, 则这些特征向量构成的空间不再是一个欧氏空间, 而原始的MS算法是定义在欧氏空间中的. 因此, 本文首先将每一个像素点的厄尔米特正定极化协方差矩阵也称为一个张量, 而且使用黎曼流形来描述该张量空间. 然后, 原始的MS算法被扩展到该张量空间中. 直接扩展得到的算法每一步具有明确的含义, 但是运算复杂度较高. 所以本文又进一步对该算法进行了简化, 从而得到了一个实用的分割算法. 通过使用真实的全PolSAR数据以及仿真数据进行实验, 结果验证了新方法的有效性.     

一种子视相干优化的极化SAR船只检测方法

针对传统单极化SAR船只检测能力不足的问题,提出了一种基于极化SAR图像子视相干的方法来检测船只。该方法在船只和海杂波SAR子视图相干程度分析的基础上,通过调整全极化SAR图像数据的最优极化状态得到3个优化的相干参数,并由此定义了一种最优相干积参数。由于该参数可以保留相干目标(即船只)的强度和相位,因此能够极大提高目标与背景之间的对比度,从而改善后期的目标检测性能。最后,采用机载极化SAR数据来评估本文方法,其试验结果表明,该方法能充分利用目标的极化特征以及子视相干性信息,显著提高了船海对比度,实现了船只检测性能的改进。     

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 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.     

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.     

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。     

高阶条件随机场引导的多分支极化SAR图像分类

目的 针对极化合成孔径雷达（polarimetric synthetic aperture radar,PolSAR）小样本分类问题,基于充分挖掘有限样本的极化、空间特征考虑,提出一种由高阶条件随机场（conditional random field,CRF）引导的多分支分类网络模型。方法 利用Yamaguchi非相干目标分解方法,构建每个像素的极化特征向量。设计了由高阶CRF能量函数引导的多卷积分支特征提取网络,将像素点极化特征向量作为输入,分别提取像素点的像素特征、邻域特征和位置特征信息。将以上特征进行加和融合,并输入到softmax分类器中得到预分类结果。利用超像素方法对预分类结果图进行进一步修正和调优,平滑相邻像素之间的特异性和相似性。结果 采用1%的采样率对两组真实的极化SAR数据进行测试。同时,为了更好地模拟实际应用中训练样本位置分布不均匀的情况,考虑了空间不相交采样方法作为对比实验。综合两种采样策略的实验结果表明,相较于只利用像素级特征或简单利用空间特征的方法,本文方法总分类精度平均提升7%～10%,不同地物类别的分类精准度均在90%以上,运行速度相比于支持向量机（support vector machine,SVM）提高了2.5倍以上。结论 通过构建高阶CRF引导的卷积神经网络,将像素特征信息、同质区域特征和地理位置信息进行融合,有效建立了像素级和对象级数据之间的尺度关联,进一步扩充了像素点之间的空间依赖性,提取到了更强大更准确的表征特征,显著提高了标记样本数量较少情况下的卷积网络模型的分类性能,进一步保证了地物目标散射机制表征的全面性和可靠性。     

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.     

一种改进的SAR图像LEE滤波算法

针对SAR图像相干斑滤波中存在的降低相干斑与有效保持细节信息这一矛盾,研究了常用空域滤波算法,在此基础上,将中值滤波与增强LEE滤波相结合,改进了LEE滤波算法,该方法能够在滤除相干斑的同时很好地保持图像的边缘及细节纹理信息。     

PolSAR ship detection based on the contrast enhancement of polarimetric scattering differences

The ship detection in polarimetric synthetic aperture radar (PolSAR) mode is a hot topic in recent years, because of the diversity of polarimetric scattering mechanisms between ship targets and sea clutter. To improve the detection performance of ship targets, this paper mainly develops the ship detection method based on the contrast enhancement utilizing the polarimetric scattering difference. The algorithm first enhances the target signal utilizing the scattering difference of the polarimetric coherency matrix between ship targets and sea clutter, and then a simple threshold is applied to distinguish the ship targets from the sea clutter. Finally, real PolSAR datasets recorded by AirSAR system are used to evaluate the effectiveness of the proposed detection method. Compared with other detection methods, experimental results indicate that the proposed method can effectively improve the detection performance of ship targets.     

Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction

This paper presents the comparative study of various wavelet filter based denoising methods according to different thresholding values applied to ultrasound images. An original image is transformed into a multi scale wavelet domain and the wavelet coefficients are processed by a soft thresholding method. The denoised image is the output image obtained from the inverse wavelet transform of the threshold coefficients using Donoho's method. It has been observed that such denoising methods are effective in the sense that they preserve the edge details besides suppressing the noise. The comparative evaluation of the denoising performance is shown using statistical significance tests for different wavelet filters. Image quality parameters such as peak signal-to-noise ratio, normalized mean square error, and correlation coefficient have been used to evaluate the performance of wavelet filters. The performance has also been compared with the adaptive weighted median filtering method.