Coastal wind field retrieval from polarimetric synthetic aperture radar

Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar （SAR） with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models （NWP）, which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex （SLC） data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful addition to the work of independent SAR wind retrieval. The experimental results herein show that it is feasible to employ the co-polarimetric backscattering coefficients and the polarimetric correlation backscattering coefficients for coastal wind field retrieval.

The polarimetric features of oil spills in full polarimetric synthetic aperture radar images

Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.

A Review on Applications of Imaging Synthetic Aperture Radar with a Special Focus on Cryospheric Studies

The cryosphere is the frozen part of the Earth’s system. Snow and ice are the main constituents of the cryosphere and may be found in different states, such as snow, freshwater ice, sea ice, perma-frost, and continental ice masses in the form of glaciers and ice sheets. The present review mainly deals with state-of-the-art applications of synthetic aperture radar (SAR) with a special emphasize on cryospheric information extraction. SAR is the most important active microwave remote sensing (RS) instrument for ice monitoring, which provides high-resolution images of the Earth’s surface. SAR is an ideal sensor in RS technology, which works in all-weather and day and night conditions to provide useful unprecedented information, especially in the cryospheric regions which are almost inaccessible areas on Earth. This paper addresses the technological evolution of SAR and its applications in studying the various components of the cryosphere. The arrival of SAR radically changed the capabilities of information extraction related to ice type, new ice formation, and ice thickness. SAR applications can be divided into two broad classes-polarimetric applications and interferometric applications. Polarimetric SAR has been effectively used for mapping calving fronts, crevasses, surface structures, sea ice, detection of icebergs, etc. The paper also summarizes both the operational and climate change research by using SAR for sea ice parameter detection. Digital elevation model (DEM) generation and glacier velocity mapping are the two most important applications used in cryosphere using SAR interferometry or interferometric SAR (InSAR). Space-borne InSAR techniques for measuring ice flow velocity and topography have developed rapidly over the last decade. InSAR is capable of measuring ice motion that has radically changed the science of glaciers and ice sheets. Measurement of temperate glacier velocities and surface characteristics by using airborne and space-borne interferometric satellite images have been the significant application in glaciology and cryospheric studies. Space-borne InSAR has contributed to major evolution in many research areas of glaciological study by measuring ice-stream flow velocity, improving understanding of ice-shelf processes, yielding velocity for flux-gate based mass-balance assessment, and mapping flow of mountain glaciers. The present review summarizes the salient development of SAR applications in cryosphere and glaciology.

Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery

Classification of intertidal area in synthetic aperture radar(SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value(GEV) statistical model of the polarization features and the Markov random field(MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

Polarimetric whitening filter for POLSAR image based on subspace decomposition

Speckle filtering is an indispensable pre-processing step for applications of polarimetric synthetic aperture radar （POLSAR）, such as terrain classification, target detection, etc. As one of the most typical methods, the polarimetric whitening filter （PWF） can be used to produce a minimum-speckle image by combining the complex elements of the scattering matrix, but polarimetric information is lost after the filtering process. A polarimetric filter based on subspaze decomposition which was proposed by Cu et al specializes in retrieving principle scattering characteristics, but the corresponding mean value of an image after filtering is not kept well. A new filter is proposed for improving the disadvantage based on subspace decomposition. Under the constraint that a weighted combination of the polarimetric SAR images equals to the output of the PWF, the Euclidean distance between an unfiltered parameter vector and a signal space vector is minimized so that noises can be reduced. It is also shown that the proposed method is equivalent to the subspace filter in the case of no constraint. Experimental results with the NASA/JPL airborne polarimetric SAR data demonstrate the effectiveness of the proposed method.

Modified version of three-component model-based decomposition for polarimetric SAR data

A new hybrid Freeman/eigenvalue decomposition based on the orientation angle compensation and the various extended volume models for polarimetric synthetic aperture radar(PolSAR) data are presented. There are three steps in the novel version of the three-component model-based decomposition.Firstly, two special unitary transform matrices are applied on the coherency matrix for deorientation to decrease the correlation between the co-polarized term and the cross-polarized term.Secondly, two new conditions are proposed to distinguish the manmade structures and the nature media after the orientation angle compensation. Finally, in order to adapt to the scattering properties of different media, five different volume scattering models are used to decompose the coherency matrix. These new conditions pre-resolves man-made structures, which is beneficial to the subsequent selection of a more suitable volume scattering model.Fully PolSAR data on San Francisco are used in the experiments to prove the efficiency of the proposed hybrid Freeman/eigenvalue decomposition.

Novel polarimetric SAR speckle filtering algorithm based on mean shift

For better interpretation of synthetic aperture radar（SAR） images,the speckle filtering is an important issue.In the area of speckle filtering,the proper averaging of samples with similar scattering characteristics is of great importance.However,existing filtering algorithms are either lack of a similarity judgment of scattering characteristics or using only intensity information for similarity judgment.A novel polarimetric SAR（PolSAR） speckle filtering algorithm based on the mean shift theory is proposed.As polarimetric covariance matrices or coherency matrices form Riemannian manifold,the pixels with similar scattering characteristics gather closely and those with different scattering characteristics separate in this hyperspace.By using the range-spatial joint mean shift theory in Riemannian manifold,the pixels chosen for averaging are ensured to be close not only in scattering characteristics but also in the spatial domain.German Aerospace Center（DLR） L-Band Experiment SAR（E-SAR） data and East China Research Institute of Electronic Engineering（ECRIEE） PolSAR data are used to demonstrate the efficiency of the proposed algorithm.The filtering results of two commonly used speckle filtering algorithms,refined Lee filtering algorithm and intensity driven adaptive neighborhood（IDAN） filtering algorithm,are also presented for the comparison purpose.Experiment results show that the proposed speckle filtering algorithm achieves a good performance in terms of speckle filtering,edge protection as well as polarimetric characteristics preservation.

Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index(NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03 % by volume.

Improved SMB speckle filtering of polarimetric SAR data with synergistic use of orientation angle compensation and spatial majority rule

The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.

Integration of SAR Polarimetric Features and Multi-spectral Data for Object-Based Land Cover Classification

An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.

Polarimetric entropy of the ocean surface with a two-scale scattering model

The relationships among an ocean wave spectrum,a fully polarimetric coherence matrix,and radar parameters are deduced with an electromagnetic wave theory.Furthermore,the relationship between the polarimetric entropy and ocean wave spectrum is established based on the definition of entropy and a twoscale scattering model of the ocean surface.It is the first time that the polarimetric entropy of the ocean surface is presented in theory.Meanwhile,the relationships among the fully polarimetric entropy and the parameters related to radar and ocean are discussed.The study is the basis of further monitoring targets on the ocean surface and deriving oceanic information with the entropy from the ocean surface.The contrast enhancement between human-made targets and the ocean surface with the entropy is presented with quad-pol airborne synthetic aperture radar（AIRSAR） data.

New algorithm of target classification in polarimetric SAR

The different approaches used for target decomposition （TD） theory in radar polarimetry are reviewed and three main types of theorems are introduced： those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine （SVM）, as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is proposed. To conduct the experiment, the polarimetric synthetic aperture radar （SAR） data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.

A method for coastal oil tank detection in polarimetric SAR images based on recognition of T-shaped harbor

To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest（ROI） of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter（SP） of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.

ADAPTIVE MODEL-BASED SCATTERING DECOMPOSITION OF POLARIMETRIC SAR INTERFEROMETRY

In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.

基于极化SAR梯度和复Wishart分类器的舰船检测

舰船检测是极化SAR系统的重要应用之一。现有的舰船检测方法容易受到旁瓣泄露的干扰,使得舰船目标的形态难以提取,导致检测结果不符合真实情况。此外,在舰船过于密集、尺度不一致的情况下,相邻舰船由于旁瓣的影响有时会被认为是单个目标,从而造成漏检。针对这些问题,该文提出一种基于极化SAR梯度和复Wishart分类器的舰船检测方法。首先,将似然比检验(LRT)梯度引入对数比值梯度框架,使其适用于极化SAR数据;基于LRT梯度图进行恒虚警(CFAR)检测,提取舰船的边缘信息,消除伪影的同时抑制强旁瓣对舰船精细轮廓提取的影响。其次,利用复Wishart迭代分类器对舰船强散射部分进行检测,可排除大部分的杂波干扰且保持舰船形态细节。最后,将二者信息融合,从而可以保持舰船形态细节的同时克服旁瓣和伪信号的虚警。该文在3幅来自ALOS-2卫星的极化SAR图像上进行了对比实验,实验表明与其他方法相比,该文所提算法具有更少的虚警和漏检,且能够有效克服旁瓣泄露,保持舰船形态细节。

面向目视解译的全极化SAR船只精细化特征表征方法

随着卫星技术的发展,极化合成孔径雷达(PolSAR)数据的分辨率和数据质量得到大幅提升,为人造目标的精细化目视解译提供了良好的数据条件。目前主要采用多分量分解的方法,但是易造成像素错分问题,为此,该文结合Yamaguchi极化分解和极化熵提出了一种非固定阈值划分的方法用于实现全极化SAR图像船只结构精细化特征表征。Yamaguchi极化分解能够识别基本散射机制,其修正后的体散射模型更符合实测数据,可有效对人造目标进行表征。极化熵H在弱去极化状态下可以看成某一指定等效点的目标散射机制,能够有效突出船只主散射特征。因此,该文通过将Yamaguchi极化分解算法的非固定三分量与极化熵的低中高熵内嵌,将其分为非固定阈值的九分类成分,从而降低硬阈值处理在阈值边界处受噪声影响产生的类别随机性。并且将二次散射和单次散射均显著的区域称为混合散射(MSM),以更好匹配实验中船只典型结构的散射类型。在此基础上,利用广义相似性参数进一步缩短类内距离,采用改进后的GSP-Wishart分类器进行迭代聚类,旨在通过提高二次散射和混合散射机制以提高不同类型船只可区分度。最后,该文采用中国上海某港口的高分三号全极化SAR数据进行实验,为了验证每艘船只特征表征正确性,通过船舶自动识别系统(AIS)收集并筛选了该港口船只信息及光学数据,并与极化SAR数据中每艘船只进行匹配。实验结果表明该方法可有效区分散货船、集装箱船和油轮3种类型船只。

基于RIME-CNN-SVR模型的麦田土壤水分反演

土壤水分监测对于农业生产和作物产量预估具有重要意义。近年来深度学习技术在土壤水分反演领域得到广泛应用,但大多侧重于模型结构增强和优化,对模型超参数优化研究探索不足。该研究提出了一种基于霜冰优化算法(rime optimization algorithm,RIME)的卷积神经网络(convolutional neural network,CNN)超参数优化模型,结合极化分解技术来校正植被对土壤水分反演精度的影响,以提高冬小麦农田土壤水分反演性能。首先利用RIME优化CNN超参数以构建RIME-CNN模型,然后使用RIME-CNN模型对特征参数进行自适应提取和挖掘,之后对这些特征参数进行正则化处理并输入到支持向量回归(support vector regression,SVR)模型,构建RIME-CNN-SVR模型进行土壤水分估算。为验证所建RIME-CNN-SVR模型的有效性,利用合成孔径雷达(synthetic aperture radar,SAR)数据结合光学遥感数据,在河南省开封市冬小麦农田区进行试验验证和精度分析。结果表明,该方法在不增加模型结构复杂性和可学习参数的前提下,显著提升了模型的预测性能,决定系数可达0.72,均方根误差为2.78%,平均绝对误差为2.20%。该研究可为农业生产提供一个更为准确、可靠的土壤水分监测手段。

基于多干扰机协同的极化SAR干扰方法

针对单极化干扰机生成的干扰会被极化合成孔径雷达(SAR)鉴别和对消的问题,本文提出了一种基于多干扰机协同的极化SAR干扰方法。该方法在不改变现有干扰机单极化发射结构的基础上,通过时序协同不同位置的多个单极化干扰机,实现对极化SAR的干扰。具体来说,根据极化SAR抗压制和欺骗干扰处理方式的不同,该方法利用不同位置的多个单极化干扰机同时发射,使极化SAR无法准确估计出干扰机发射天线极化比,从而无法对消压制干扰,实现压制干扰的目的;使用不同位置的多个单极化干扰机交替发射,有效破坏相邻两个接收脉冲之间的相关性,使极化SAR无法实现假目标对消,从而实现对极化SAR欺骗干扰的目的,并且多部单极化干扰机交替发射也能实现方位向间歇采样的干扰效果,增加欺骗干扰生成虚假目标的复杂性。由于实际应用中不需要改变现有单极化干扰机发射结构,该文所提方法为实现对极化SAR干扰带来了便利。并且该方法在生成压制和欺骗干扰时均使用模板调制干扰方式,通过简单的模板修改就能实现可控的压制和欺骗干扰效果。文中首先分析了极化SAR抗压制和欺骗干扰的方法,然后分别给出了多干扰机生成压制和欺骗干扰的时序协同方式,最后以模板调制干扰为基础具体分析了多干扰机协同生成干扰的方法,并分析了多干扰机的布局和干扰机数量选取准则。理论推导和仿真实验证明了本文方法能够实现对极化SAR的干扰。

基于融合距离的极化SAR图像非局部均值滤波

在极化合成孔径雷达(synthetic aperture radar,SAR)图像降噪领域,常见的非局部均值滤波仅依靠像素间的统计距离进行相似性度量,忽略了像素点的空间信息。本文结合极化SAR数据统计特性和图像空间特征作为像素间的相似性度量,提出了一种利用融合距离来计算相邻窗口权重的方法——基于融合距离的非局部均值滤波器。融合距离的引入使得滤波器能够更全面的评估像素间的相似性,从而得到更合适的像素权重。此外,本方法还引进变异系数对邻域窗口的权重进行评估,通过该参数可以控制滤波的程度。在多幅极化SAR图像上的实验结果表明,所提出的滤波器能够在有效抑制斑点噪声的同时保留较为完整的图像边缘信息和极化散射特性。

基于多源遥感图像多级协同融合的舰船识别算法

针对极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像存在斑点噪声严重、可视性差、直接影响目标识别精度的问题,提出一种基于多源遥感图像多级协同融合的舰船识别算法。通过采用多级协同融合方式,丰富图像的特征量,提高舰船识别精度。所提方法首先进行多源遥感数据的像素级融合,然后在上一步基础上进行特征级融合,最终得到新的目标特征。所提方法充分发挥了不同频段的PolSAR与多光谱图像的信息互补优势,不仅保留了多频段PolSAR对目标的极化散射特征,也保留了多光谱数据的空-谱信息。所提方法在可视性与检测精度上表现都较为出色,与传统的单一遥感数据相比,识别精度至少提高了5.12%。