全极化SAR数据信息提取研究

全极化SAR(Synthetic Aperture Radar)测量的是每一像元的全散射矩阵,可合成包括线性极化、圆极化及椭圆极化在内的多种极化图像。因此与常规的单极化和多极化SAR相比,在雷达目标探测、识别、纹理特征的提取等方面全极化SAR具有很多优点。基于新疆和田地区的SIR-C L波段全极化雷达数据,介绍了极化合成的基本原理和数据处理流程,分析了几种典型地物全极化信号的特点,并在此基础上用监督分类法进行了全极化SAR数据的信息提取。结果表明:全极化SAR数据比单极化和多极化SAR数据具有更高的分类精度,并有效地的提取出地表信息,为利用SAR数据反演地表参数打下了基础。     

基于目标分解理论的全极化SAR图像神经网络分类方法

由于全极化合成孔径雷达(synthetic aperture radar)能够测量每一观测目标的全散射矩阵，即可合成包括线性极化、圆极化及椭圆极化在内的多种极化图像，因此与常规的单极化和多极化SAR相比，在雷达目标探测、识别，纹理特征和几何参数的提取等方面，全极化SAR均具有很多优点，但是由于地物分布的复杂性往往造成不同地物具有相似的后向散射信号特征，因而加大了地物信息提取的难度。同时由于这些极化合成图像具有较高的相关性，从而导致了图像分类精度的降低。为了提高全极化SAR图像的分类精度，基于新疆和田地区的SIR-CL波段全极化雷达数据，利用目标分解理论首先将地物回波的复杂散射过程分解为几种互不相关的单一的散射分量。由于这些单一的散射分量都对应于具有不同物理和几何特征以及分布特征的地物，从而提供了更加丰富的地表覆盖信息，这样就很大程度地改善了地物信息的分类精度；然后利用分解后单一散射分量数据结合传统的极化合成数据，可以得到更多的互不相关的数据源，再使用神经网络分类法对这些数据进行分类。分类结果表明，这种方法大幅度提高了全极化SAR数据用于实验区土地覆盖分类的精度。这种分类方法也可以广泛地用于SAR数据地表覆盖和土地利用动态监测和地表参数的提取。     

一种新的简缩极化SAR图像H/α特征空间

现有简缩极化(Compact Polarimetry)SAR图像H/α经验特征空间存在两个问题:一是没有考虑简缩极化模式下的散射熵普遍高于全极化模式;二是在散射机制重叠区域,简缩极化H/α空间的分类能力较弱,尤其是多次散射。针对以上问题首先定量分析了DCP模式简缩极化SAR的散射角与全极化SAR数据散射角之间的关系,且在对7组不同传感器的SAR数据分析的基础上,提出了散射熵的替代参数ED,基于Monte Carlo模拟实验得到了H/α分解的各参数(熵H、平均散射角α和替代参数ED)分解的稳态条件;然后通过统计各散射机制在ED/α分布的密度空间,提出了一种新的简缩极化SAR图像ED/α特征空间。实验结果表明:替代参数ED与全极化熵具有良好相关性,而且ED/α特征空间提高了散射机制分类的精度。     

极化SAR图像地物分类方法综述

极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)是一种微波成像雷达, 它不受天气、光线以及云层的影响, 能够实现全天时、全天候的成像. 因此, 极化SAR图像已经成为遥感图像地物分类的主要数据源之一. 本文从技术方法的角度出发, 论述了近年来国内外极化SAR图像地物分类的方法及应用, 从技术原理、实验效果等方面进行介绍, 并对极化SAR图像地物分类的发展趋势进行分析.     

基于特征向量分解与散射机制判别指数的全极化SAR图像地物提取与分类

基于特征向量分解和基于散射模型的极化目标分解是全极化SAR非相干分解中的典型算法。本文对比研究了两种算法的特点及分解结果在地物识别分类方面的优势,在基于特征向量分解得到的H-Alpha特征平面的基础之上,引入散射机制判别指数来刻画地物的类别差异,从而能约束H-Alpha平面分割的界限以提高分类的精度,而且利用散射机制占优性强弱可辅助分类结果的解译。实验选取了鄱阳湖地区一景Radarsat-2标准全极化数据,实验结果对比表明一种散射机制占主导的地物,分类精度得到改善,特别是水域、形成二面角的目标区和成片分布的植被区域可以显著地提取出来。     

基于多层支持向量机的极化合成孔径雷达特征分析与分类

为了充分利用极化合成孔径雷达（SAR）图像不同极化特征对不同地物目标类型的刻画能力,提出一种基于多层支持向量机（SVM）的极化SAR特征分析与分类方法。该方法首先通过特征分析确定适合不同地物类型的最佳特征子集;然后采用分层分类树的方式,根据每一种地物类型的特征子集逐层进行SVM分类;最终得到整体分类结果。RadarSAT-2极化SAR图像分类实验结果表明所提方法水域、耕地、林地、城区4类地物分类精度为85%左右,总体分类精度达到86%。该算法充分利用了不同地物目标类型的特性,提高了分类精度,也降低了算法时间复杂度。     

基于特征值分解的全极化SAR遥感数据分类研究

应用极化目标特征值分解理论,研究了全极化合成孔径雷达图像的精细分类问题,在H-α-Wishart分类基础上引入平均散射功率,并根据不同地物的散射功率强度信息,给出了一种简单的阈值分割方法,最后利用鄱阳湖地区的Radarsat-2全极化数据进行了实验和分析,结果发现引入平均散射功率信息后的分类类别更多、精度更好。     

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。     

简述极化SAR定标处理技术研究进展

极化定标是极化SAR数据用于图像解译和定量参数反演的关键步骤。综述了国内外极化SAR定标技术及应用研究的主要成果,着重介绍极化SAR定标技术发展以来取得的多种关键算法进展,系统梳理包括点目标定标算法、分布目标定标算法、法拉第旋转校正算法等技术原理介绍、参数定义估计及技术脉略发展分析,并讨论各技术方法在机载及星载SAR系统定标领域取得的应用发展现状,同时逐步分解该技术领域目前面临的技术难点及算法发展。最后探讨分析极化SAR定标技术发展中的未来需求难点和继续需要解决的问题,为研究人员进一步推动极化SAR定标技术发展提供参考。     

全极化SAR极化特征谱应用研究

在分析特征值分解结果,全部散射机制组合和极化特征谱性质的基础上,提出基于3个特征谱参数的假彩色合成方法,可以更加有效直观地反映地物散射特征,再对散射熵、散射角、反熵和4个极化特征谱参数进行特征选择分析,给出最佳的多维特征向量选择方案,从而实现传统遥感图像分类器如同ISODATA算法对极化SAR图像的分类。实验选择了一景Radarsat\|2标准全极化SAR数据,包含典型的城市、植被和水体三大类地物,实验结果表明:极化特征谱假彩色合成充分反映了各地物散射特征,特征谱和散射角组成了最佳特征向量,非监督分类结果表明:该方法克服了城市与植被在H\|Alpha平面上分布界限模糊的问题,分类精度高于H\|Alpha平面非监督分类,与Wishart-H-Alpha-A分类方法相当。     

Application of incoherent target decomposition theorems to classify snow cover over the Himalayan region

Snow cover is an important parameter for hydrological modelling and climate change modelling. Various methods are available only for wet snow-cover mapping using conventional synthetic aperture radar (SAR) data. Total snow (wet + dry) cover mapping with SAR data is still a topical research area. Therefore, incoherent target decomposition theorems have been implemented on fully polarimetric SAR data to characterize the scattering of various targets. Further classification techniques – both unsupervised and supervised – have been applied for accurate mapping of total snow cover. For this purpose, Advanced Land Observing Satellite – phased array-type L-band SAR (ALOS–PALSAR) data (12 May 2007) have been analysed for snow classification of glaciated terrain in and around Badrinath region in Himalaya. An ALOS-Advanced Visible and Near Infrared Radiometer (AVNIR)-2 image (6 May 2007) was also used to provide assistance in the selection of different training classes. It has been found that the application of incoherent target decomposition theorems such as H/A/α and four-component scattering mechanism models are good for extracting the desired information of snow cover from fully polarimetric PALSAR data. Finally, based on these target decomposition theorems and the Wishart classifier, PALSAR data have been classified into snow or non-snow cover, and the user accuracy of snow classes was found to be better than the user accuracy of other classes. Hence, the application of incoherent target decomposition theorems with full polarimetric ALOS-PALSAR data is useful for snow-cover mapping.     

Improving SAR data processing with polarimetric reference functions in the range Doppler algorithm

Synthetic aperture radar (SAR) is a form of radar that can be used to create images of objects and landscapes. The main important application of the polarimetric SAR can be found in surface and target decomposition process of its image processing. In this article, we propose a method of polarimetric SAR data processing using two new polarimetric reference functions of canonical targets with the intention to apply in coherent decompositions. Our experiment uses polarimetric backscatter characteristics of the dihedral and trihedral reflectors as the targets under a ground-based SAR geometry to create the polarimetric reference functions for azimuth compression in the SAR data processing. We process the data using Pauli decomposition to investigate the effect of our functions on the RGB (red, green, and blue) properties of the processed images. The results show that Pauli decomposition using our functions produces images with different distribution and intensity of RGB colours in the image pixels with some signs of improvement over the traditional range Doppler algorithm. This demonstrates that our polarimetric reference function can be used in the decomposition steps of the traditional SAR data processing and can potentially be used to reveal some useful quantitative physical information of target points of interest and improve image and surface classification.     

Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution. This algorithm can also be applied to multifrequency multi-look polarimetric SAR data, as well as 10 SAR data containing only intensity information. A procedure is then developed for unsupervised classification.

The classification error is assessed by using Monte Carlo simulation of multilook polarimetric SAR data, owing to the lack of ground truth for each pixel. Comparisons of classification errors using the training sets and single-look data are also made. Applications of this algorithm are demonstrated with NASA/JPL P-, L- and C-band polarimetric SAR data.     

基于对象的合成孔径雷达影像极化分解方法

面向对象方法已成为全极化合成孔径雷达（SAR）影像处理的常用方法,但是极化分解仍以组成对象的像素为计算单元,针对以像素为单位的极化分解效率低的问题,提出一种面向对象的极化分解方法。通过散射相似性系数加权迭代,获得对象的极化表征矩阵并对其收敛性进行了分析,以对象极化表征矩阵的极化分解代替对象区域内所有像素的分解,提高极化特征获取效率。在此基础上,综合影像对象空间特征,并通过特征选择与支持向量机(SVM)分类进行分析和评价。通过AIRSAR Flevoland影像数据实验表明,面向对象的分解方法能够减少对象极化特征提取的时间,同时提高地物目标的分类精度。相对于监督Wishart方法,提出方法的总体精度和Kappa值分别提高了17%和20%。     

Whitt点目标极化定标算法的实验研究

将Whitt等人提出的通用的点目标极化定标算法应用到SIR-C系统获取的L波段多极化数据中,进行定标实验。利用所获取的SIR-C合成孔径雷达图像中存在的点目标,将它们经过SIR-C定标组定标的极化数据作为理论值看待,运用Whitt算法来定标整幅没有定标的图像。最后将实验获取的结果与SIR-C定标组定标结果互相比较,可以看出这个算法具有良好的性能。     

A unified framework for crop classification in southern China using fully polarimetric,dual polarimetric,and compact polarimetric SAR data

With the development of synthetic aperture radar (SAR) techniques, various imaging modes that involve single polarimetry, dual polarimetry, full polarimetry (FP), and compact polarimetry (CP) have been proposed and applied to SAR systems. This article attempts to introduce a unified framework for crop classification in southern China using FP, coherent HH/VV, and CP data. By analysing the polarimetric response from different land-cover types (including rice, banana trees, sugarcane, eucalyptus, water, and built-up areas in the experimental site) and by exploring the similarities between data in these three modes, a knowledge-based characteristic space is created and a unified classification framework is presented. Time-series data acquired by TerraSAR-X over the Leizhou Peninsula, southern China, are used in our experiments. The overall classification accuracies for data in the FP and coherent HH/VV modes are approximately 95%, and for data in the CP mode, the accuracy is 91%, which suggest that the proposed classification scheme is effective. Compared with the Wishart Maximum Likelihood (ML) classifier, the proposed method provides approximately 5.64%, 7.30%, and 6.48% higher classification accuracies in the FP, HH/VV, and circular transmit and dual circular receive modes, respectively.     

A novel approach to polarimetric SAR data processing based on Nonlinear PCA

In remotely sensed Synthetic Aperture Radar (SAR) images, scattering from a target is often the result of a mixture of different mechanisms. For this reason, detection of targets and classification of SAR images may be very difficult and very different from other sensor imagery. Fully polarimetric data offer the possibility to separate the different mechanisms, interpret them and consequently identify the geometry of the targets. To achieve this task, several target decomposition techniques have been proposed in the literature to improve the interpretation of this kind of data. Among these, the physical based techniques are the most considered.     

Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

The ability of synthetic aperture radar (SAR) C-band microwave energy to penetrate within forest vegetation makes it possible to extract information on crown components, which in turn gives a better approximation of relative canopy density than optical data-derived canopy density. Many studies have been reported to estimate forest biomass from SAR data, but the scope of C-band SAR in characterizing forest canopy density has not been adequately understood with polarimetric techniques. Polarimetric classification is one of the most significant applications of polarimetric SAR in remote sensing. The objective of the present study was to evaluate the feasibility of different polarimetric SAR data decomposition methods in forest canopy density classification using C-band SAR data. Landsat (Land Satellite) 5 TM (Thematic Mapper) data of the same area has been used as optical data to compare the classification result. RADARSAT (Radar Satellite)-2 image with fine quad-pol obtained on 27 October 2011 over tropical dry forests of Madhav National Park, India, was used for the analysis of full polarimetric data. Six decomposition methods were selected based on incoherent decomposition for generating input images for classification, i.e. Huynen, Freeman and Durden, Yamaguchi, Cloude, Van zyl, and H/A/α. The performance of each decomposition output in relation to each land cover unit present in the study area was assessed using a support vector machine (SVM) classifier. Results show that Yamaguchi 4-component decomposition (overall accuracy 87.66% and kappa coefficient (κ) 0.86) gives better classification results, followed by Van Zyl decomposition (overall accuracy 87.20% and κ 0.85) and Freeman and Durden (overall accuracy 86.79% and κ 0.85) in forest canopy density classification. Both model-based decompositions (Freeman and Durden and Yamaguchi4) registered good classification accuracy. In eigenvector or eigenvalue decompositions, Van zyl registered the second highest accuracy among different decompositions. The experimental results obtained with polarimetric C-band SAR data over a tropical dry deciduous forest area imply that SAR data have significant potential for estimating canopy density in operational forestry. A better forest density classification result can be achieved within the forest mask (without other land cover classes). The limitations associated with optical data such as non-availability of cloud-free data and misclassification because of gregarious occurrence of bushy vegetation such as Lantana can be overcome by using C-band SAR data.     

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.