Observation of sea-ice thickness in the sea of Okhotsk by using dual-frequency and fully polarimetric airborne SAR (pi-SAR) data

To investigate the possibilities of using dual-frequency, multipolarization synthetic aperture radar (SAR) data to monitor sea ice, we derived the relationship between various polarization characteristics and the physical parameters of sea ice. We discuss the frequency and polarization characteristics of the backscattering coefficients of sea ice and then characterize its thickness by comparing the corresponding backscattering coefficient for each polarization with the physical parameters of the ice. We first propose a methodology for classifying sea-ice types by using a polarimetric decomposition technique, before comparing an estimation of the sea-ice thickness with the corresponding dual-frequency, multipolarization SAR data. We utilized the backscattering ratio to estimate the thickness of the sea ice. This ratio canceled the effect of roughness on the backscattering. The method was validated using Pi-SAR (polarimetric and interferometric airborne SAR) observation data obtained at ground-truth sites.     

Application of neural networks for sea ice classification inpolarimetric SAR images

Several automatic methods have been developed to classify sea ice types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are generally grouped into supervised and unsupervised approaches. In previous work, supervised methods have been shown to yield higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new classification technique is applied to determine sea ice types in polarimetric and multifrequency SAR images, utilizing an unsupervised neural network to provide automatic classification, and employing an iterative algorithm to improve the performance. The learning vector quantization (LVQ) is first applied to the unsupervised classification of SAR images, and the results are compared with those of a conventional technique, the migrating means method. Results show that LVQ outperforms the migrating means method, but performance is still poor. An iterative algorithm is then applied where the SAR image is reclassified using the maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. The new algorithm successfully identifies first-year and multiyear sea ice regions in the images at three frequencies. The results show that L- and P-band images have similar characteristics, while the C-band image is substantially different. Classification based on single features is also carried out using LVQ and the iterative ML method. It is found that the fully polarimetric classification provides a higher accuracy than those based on a single feature. The significance of multilook classification is demonstrated by comparing the results obtained using four-look and single-look classifications     

融合极化旋转域特征和超像素技术的极化SAR舰船检测

对海监视是极化SAR的重要应用,密集区域的舰船目标检测是当前面临的主要挑战之一。舰船密集区域受多目标串扰,传统的恒虚警率(CFAR)检测滑窗难以选取纯净的海杂波样本用于确定检测门限,将导致检测性能下降。针对这一问题,该文从特征提取和检测器设计两方面出发,提出一种融合极化旋转域特征和超像素技术的极化SAR舰船检测方法。在特征提取方面,雷达目标的后向散射敏感于目标姿态与雷达视线的相对几何关系,由此带来的散射多样性隐含信息可通过极化旋转域分析进行挖掘。该文利用极化相关方向图及导出的一系列极化旋转域特征,根据目标杂波比(TCR)分析,优选TCR最高的3个极化特征量用于构建目标检测器。在此基础上,该文在检测器设计方面提出了一种基于K均值聚类的杂波超像素筛选方法,有效避免了密集区域舰船目标对邻近杂波的影响,基于筛选的杂波像素点得到舰船目标CFAR检测结果。基于Radarsat-2和高分三号星载全极化SAR数据的对比实验表明,所提方法能有效实现密集区域舰船目标检测,检测品质因数达到95%。      

Diurnal thermal cycling effects on microwave signatures of thin seaice

To investigate effects of diurnal thermal cycles on C-band polarimetric backscatter and millimeter-wave emission from sea ice, the authors carried out a winter experiment at the outdoor geophysical research facility (GRF) in the cold regions research and engineering laboratory (CRREL), the ice sheet grew from open sea water to a thickness of 10 cm in 2.5 days, during which they took polarimetric backscatter data with a C-band scatterometer, interlaced with brightness temperature measurements at 90 GHz in conjunction with meteorological and sea ice characterizations. The initial ice growth in the late morning was slow due to high insolation. As the air temperature dropped during the night, the growth rate increased significantly. Air temperature changed drastically from about -12 to -36°C between day and night, the diurnal thermal cycle repeated itself the next day and the growth rate varied in the same manner. Ice temperature profiles clearly show the diurnal response in the ice sheet with a lag of 2.5 h behind the time of the maximum short-wave incident solar radiation. The diurnal cycles are also evident in the millimeter-wave brightness temperature data, measured sea ice backscatter revealed substantial diurnal variations up to 6 dB with repeatable cycles in synchronization with the temperature cycles and the brightness temperature modulations, the diurnal cycles in backscatter indicate that the dominant scattering mechanism related to thermodynamic processes in sea ice is reversible, a diurnal backscatter model based on sea ice electrodynamics and thermodynamics explains the observed diurnal signature. This work shows that diurnal effects are important for inversion algorithms to retrieve sea ice geophysical parameters from remote sensing data acquired with a satellite synthetic aperture radar (SAR) or scatterometer on Sun-synchronous orbits     

Unsupervised segmentation of polarimetric SAR data using thecovariance matrix

A method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics is presented. Classes of polarimetric backscatter are selected on the basis of a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses both polarimetric amplitude and phase information, is adapted to the presence of image speckle, and does not require an arbitrary weighting of the different polarimetric channels; it also provides a partitioning of each data sample used for clustering into multiple clusters. Given the classes of polarimetric backscatter, the entire image is classified using a maximum a posteriori polarimetric classifier. Four-look polarimetric SAR complex data of lava flows and of sea ice acquired by the NASA/JPL airborne polarimetric radar (AIRSAR) are segmented using this technique     

Probability density functions for multilook polarimetric signatures

Derives closed-form expressions for the probability density functions (PDF's) for copolar and cross-polar ratios and for the copolar phase difference for multilook polarimetric SAR data, in terms of elements of the covariance matrix for the backscattering process. The authors begin with the case in which scattering-matrix data are jointly Gaussian-distributed. The resulting copolar-phase PDF is formally identical to the phase PDF arising in the study of SAR interferometry, so the authors' results also apply in that setting. By direct simulation, they verify the closed-form PDF's. They show that estimation of signatures from averaged covariance matrices results in smaller biases and variances than averaging single-look signature estimates. They then generalize their derivation to certain cases in which backscattered intensities and amplitudes are K-distributed. They find in a range of circumstances that the PDF's of polarimetric signatures are unchanged from those derived in the Gaussian case. They verify this by direct simulation, and also examine a case that fails to satisfy an important assumption in their derivation. The forms of the signature distributions continue to describe data well in the latter case, but parameters in distributions fitted to (simulated) data differ from those used to generate the data. Finally, the authors examine samples of K-distributed polarimetric SAR data from Arctic sea ice and find that their theoretical distributions describe the data well with a plausible choice of parameters. This allows the authors to estimate the precision of polarimetric-signature estimates as a function of the number of SAR looks and other system parameters     

基于支持向量机的粗糙海面风速及海表盐度反演研究

将支持向量机(Support Vector Machine, SVM)回归技术应用到海况参数(如海表盐度、海面风速等)反演研究.利用双尺度模型(Two-Scale Model, TSM)作为前向电磁算法, 数值模拟不同雷达参数下风驱粗糙海面微波后向散射系数, 经过敏感性分析, 选取L波段(1.4 GHz)、C波段(6.8 GHz)及其合适的入射角作为雷达参数, 并设计多种反演方案, 分别以单频率双极化双角度、双频率双极化双角度及双极化后向散射系数的比值作为SVM的训练样本数据信息, 经过适当的训练, 利用SVM回归技术对海洋表面风速和盐度进行了反演研究.研究结果表明, 针对于海面风速的反演, C波段的反演精度最高, 针对于海表盐度的反演, L波段同极化散射系数比值作为SVM输入的反演精度较高.最后, 检验了SVM反演方法的抗噪声性能, 表明文中提出的SVM方法能较好地应用于实际海况参数反演问题.     

Sea Ice Deformation State From Synthetic Aperture Radar Imagery—Part II: Effects of Spatial Resolution and Noise Level

C- and L-band airborne synthetic aperture radar (SAR) imagery acquired at like- and cross-polarizations over sea ice under winter conditions is examined with the objective to study the discrimination between level ice and ice deformation features. High-resolution low-noise data were analyzed in the first paper. In this second paper, the main topics are the effects of spatial resolution and signal-to-noise ratio. Airborne high-resolution SAR scenes are used to generate a sequence of images with increasingly coarser spatial resolution from 5 to 25 m, keeping the number of looks constant. The signal-to-noise ratio is varied between typical noise levels for airborne imagery and satellite data. Areal fraction of deformed ice and average deformation distance are determined for each image product. At L-band, the retrieved values of the areal fraction get larger as the image resolution is degraded. The areal fraction at C-band remains constant. The retrieved average distance between deformation features increases both at C- and L-bands as the image resolution gets coarser. The influence of noise becomes noticeable if its level is equal or larger than the average intensity backscattered from the level ice. The retrieval of deformation parameters using simulated images that resemble ERS-2 SAR, Envisat ASAR, and ALOS PALSAR data products is discussed. Basic differences between real and simulated ERS-2 SAR images are analyzed.      

A microwave backscattering model for deformed first-year sea iceand comparisons with SAR data

Like-polarized backscattering from randomly tilted ice blocks in deformed first-year sea ice is modeled. An approximation for the coherent and incoherent scattering cross section of a single ice block is formulated and validated by comparison with moment method computations. It is found that the model is accurate for lossy ice blocks but underestimates the scattering when the loss is low, which is attributed to multiple scattering within the blocks. The backscattering coefficient is evaluated by averaging over an ensemble of blocks with a distribution of slopes and effects of shadowing are estimated. In situ measurements of ice ridge properties in the Baltic Sea are used as input when comparing the model results with coincident ERS-1 SAR data. The model is found to agree with the data to within 1.5 dB, where the discrepancies are mainly due to the uncertainty of the dielectric loss factor in the ice blocks. The model also shows good agreement with airborne 5.3 GHz SAR data of a first-year shear ridge in the Beaufort Sea for incidence angles between 25-50     

Fusion of satellite active and passive microwave data for sea icetype concentration estimates

Young first-year sea ice is nearly as important as open water in modulating heat flux between the ocean and atmosphere in the Arctic. Just after the onset of freeze-up, first-year ice is in the early stages of growth and will consist of young first-year and thin ice. The distribution of sea ice in this thickness range impacts heat transfer in the Arctic. Therefore, improving the estimates of ice concentrations in this thickness range is significant. The NASA Team Algorithm (NTA) for passive microwave data inaccurately classifies sea ice during the melt and freeze-up seasons because it misclassifies multiyear ice as first-year ice. We developed a hybrid fusion technique for incorporating multiyear ice information derived from synthetic aperture radar (SAR) images into a passive microwave algorithm to improve ice type concentration estimates. First, we classified SAR images using a dynamic thresholding technique and estimated the multiyear ice concentration. Then we used the SAR-derived multiyear ice concentration to constrain the NTA and obtained an improved first-year ice concentration estimate. We computed multiyear and first-year ice concentration estimates over a region in the eastern-central Arctic in which field observations of ice and in situ radar backscatter measurements were performed. The fused estimates of first-year and multiyear ice concentration appear to be more accurate than NTA, based on ice observations that were logged aboard the US Coast Guard Icebreaker Polar Star in the study area during 1991     

Unsupervised segmentation of synthetic aperture Radar sea ice imagery using a novel Markov random field model

Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This work uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.     

Maximum likelihood estimation of K distribution parameters for SARdata

The K distribution has proven to be a promising and useful model for backscattering statistics in synthetic aperture radar (SAR) imagery. However, most studies to date have relied on a method of moments technique involving second and fourth moments to estimate the parameters of the K distribution. The variance of these parameter estimates is large in cases where the sample size is small and/or the true distribution of backscattered amplitude is highly non-Rayleigh. The present authors apply a maximum likelihood estimation method directly to the K distribution. They consider the situation for single-look SAR data as well as a simplified model for multilook data. They investigate the accuracy and uncertainties in maximum likelihood parameter estimates as functions of sample size and the parameters themselves. They also compare their results with those from a new method given by Raghavan (1991) and from a nonstandard method of moments technique; maximum likelihood parameter estimates prove to be at least as accurate as those from the other estimators in all cases tested, and are more accurate in most cases. Finally, they compare the simplified multilook model with nominally four-look SAR data acquired by the Jet Propulsion Laboratory AIRSAR over sea ice in the Beaufort Sea during March 1988. They find that the model fits data from both first-year and multiyear ice well and that backscattering statistics from each ice type are moderately non-Rayleigh. They note that the distributions for the data set differ too little between ice types to allow discrimination based on differing distribution parameters     

Evaluation of late summer passive microwave Arctic sea iceretrievals

The melt period of the Arctic sea ice cover is of particular interest in studies of climate change due to the albedo feedback mechanisms associated with meltponds and openings in the ice pack. The traditionally used satellite passive microwave sea ice concentration algorithms have deficiencies during the summer months due to the period's highly variable surface properties. A newly developed ice concentration algorithm overcomes some of these deficiencies. It corrects for low ice concentration biases caused by surface effects through the use of 85 GHz data in addition to the commonly used 19 and 37 GHz data and, thus, the definition of an additional ice type representing layering and inhomogeneities in the snow layer. This new algorithm will be the standard algorithm for Arctic sea ice concentration retrievals with the EOS Aqua advanced microwave scanning radiometer (AMSR-E) instrument. In this paper, we evaluate the performance of this algorithm for the summer period of 1996 using data from the special sensor microwave imager (SSM/I) which has frequencies similar to the AMSR instrument. The temporal evolution of summertime passive microwave sea ice signatures are investigated and sea ice concentration retrievals from the standard NASA team and the new algorithm are compared. The results show that the introduction of the additional sea ice type in the new algorithm leads to improved summertime sea ice concentrations. The SSM/I sea ice retrievals are validated using SAR-derived ice concentrations that have been convolved with the SSM/I antenna pattern to ensure an appropriate comparison. For the marginal ice zone, with ice concentrations ranging from 40% to 100%, the correlation coefficient of SAR and SSM/I retrievals is 0.66 with a bias of 5% toward higher SAR ice concentrations. For the central Arctic, where ice concentrations varied between 60% and 100%, the correlation coefficient is 0.87 with a negligible bias     

Surface roughness characterizations of sea ice and ice sheets: case studies with MISR data

This work is an examination of potential uses of multiangular remote sensing imagery for mapping and characterizing sea ice and ice sheet surfaces based on surface roughness properties. We use data from the Multi-angle Imaging SpectroRadiometer (MISR) to demonstrate that ice sheet and sea ice surfaces have characteristic angular signatures and that these angular signatures may be used in much the same way as spectral signatures are used in multispectral classification. Three case studies are examined: sea ice in the Beaufort Sea off the north coast of Alaska, the Jakobshavn Glacier on the western edge of the Greenland ice sheet, and a region in Antarctica south of McMurdo station containing glaciers and blue-ice areas. The MISR sea ice image appears to delineate different first-year ice types and, to some extent, the transition from first-year to multiyear ice. The MISR image shows good agreement with sea ice types that are evident in concurrent synthetic aperture radar (SAR) imagery and ice analysis charts from the National Ice Center. Over the Jakobshavn Glacier, surface roughness data from airborne laser altimeter transects correlate well with MISR-derived estimates of surface roughness. In Antarctica, ablation-related blue-ice areas, which are difficult to distinguish from bare ice exposed by crevasses, are easily detected using multiangular data.     

Extraction of power line maps from millimeter-wave polarimetric SARimages

Radar backscatter of power lines has lower values than those of the surrounding ground clutter when the power line is oriented at an off-normal direction with respect to the radar line of sight. For power lines, the traditional detection algorithms that are commonly based on the statistics of the backscatter power of the clutter and target result in excessive false-alarm rates due to very low signal-to-clutter ratio. The application of a statistical polarimetric detection algorithm that significantly improves the signal-to-clutter ratio is demonstrated. The coherence between the co- and cross-polarized backscatter components is used as the detection parameter. This statistical detection parameter can be applied to any extended targets such as a suspended cable in clutter background. Detection criteria based on clutter backscattering coefficients, power line size, and aspect angle, as well as the number of independent samples are obtained. The performance of the algorithm for mapping power lines in SAR images is demonstrated using a number of low-grazing incidence polarimetric SAR images at 35 GHz     

顺轨干涉SAR浅海地形成像建模及其最优雷达观测参数分析

该文基于浅海海流动力模型、波流交互模型、后向散射模型以及Doppler谱模型对顺轨干涉SAR浅海地形成像进行了建模.利用建立的成像模型,在准1维近似的条件下对不同雷达频率、入射角、顺轨基线长度以及极化方式等参数对顺轨干涉SAR浅海地形成像的影响进行了探讨.在成像延时低于海面去相关时间的前提下,高频率、大入射角以及长顺轨基线条件比较适合于顺轨干涉SAR浅海地形成像;而极化方式对顺轨干涉相位变化的影响不大,如果综合考虑信噪比等因素的影响,VV极化仍是顺轨干涉SAR浅海地形成像的首选极化方式.     

Determination of the age distribution of sea ice from Lagrangianobservations of ice motion

A procedure for monitoring the local age distribution of the Arctic sea ice cover is presented. The age distribution specifies the area covered by ice in different age classes. In the authors' approach, a regular array of grid points is defined initially on the first image of a long time series, and an ice tracker finds the positions of those points in all subsequent images of the series. These Lagrangian points mark the corners of a set of cells that move and deform with the ice cover. The area of each cell changes with each new image or time step. A positive change indicates that ice in a new age class was formed in the cell. A negative change is assumed to have ridged the youngest ice in the cell, reducing its area. The ice in each cell ages as it progresses through the time series. The area of multiyear ice in each cell is computed using an ice classification algorithm. Any area that is not accounted for by the young ice or multiyear ice is assigned to a category of older first-year ice. The authors thus have a fine age resolution in the young end of the age distribution, and coarse resolution for older ice. The age distribution of the young ice can be converted to a thickness distribution using a simple empirical relation between accumulated freezing-degree days and ice thickness, or using a more complicated thermodynamic model. The authors describe a general scheme for implementing this procedure for the Arctic Ocean from fall freeze-up until the onset of melt in the spring. The concept is illustrated with a time series of five ERS-1 SAR images spanning a period of 12 days. Such a scheme could be implemented with RADARSAT SAR imagery to provide basin-wide ice age and thickness information     

Retrieving snowpack properties and accumulation estimates from a combination of SAR and scatterometer measurements

This study combines two satellite radar techniques, low-resolution C-/Ku-band scatterometer and high-resolution C-band synthetic aperture radar (SAR) for glaciological studies, in particular mass-balance estimations. Three parameters expressing the mean backscattering and its dependency on azimuth and incidence angle are used to describe and classify the Antarctic ice sheets backscattering behavior. Simple linear regression analyses are carried out between ground truth accumulation data and the SAR backscattering coefficient along continuous profile lines. From this we parameterize the accumulation rate separately for certain snow pack regimes. We find that SAR data can be used to map mass-balance changes, however only within limited areas. Applying this method therefore generally requires accurate ground truth for regional calibration together with additional information regarding mean air temperature or elevation. This investigation focuses on the area of Dronning Maud Land, Antarctica. We present the first high-resolution accumulation map based on SAR data for the surrounding area of the EPICA deep ice core drilling site Kohnen, which is compared to reliable ground truth records as well as to a surface-mass-balance map interpolated from these at low resolution.     

Polarimetric measurements of sea surface brightness temperaturesusing an aircraft K-band radiometer

Presents the first experimental evidence that the polarimetric brightness temperatures of sea surfaces are sensitive to ocean wind direction in the incidence angle range of 30 to 50°. The experimental data were collected by a K-band (19.35 GHz) polarimetric wind radiometer (WINDRAD) mounted on the NASA DC-8 aircraft. A set of aircraft radiometer flights was successfully completed in November 1993. The authors performed circle flights over National Data Buoy Center (NDBC) moored buoys deployed off the northern California coast, which provided ocean wind measurements. The results indicate that passive polarimetric radiometry has a strong potential for global ocean wind speed and direction measurements from space     

Seasonal and short-term variability of multifrequency, polarimetricradar backscatter of Alpine terrain from SIR-C/X-SAR and AIRSARdata

Signatures of glaciated and ice free areas were analyzed from polarimetric SAR data at C-, L-, and P-band and single polarization X-band data. The data base includes an AIRSAR scene from June 25, 1991, and SIR-C/X-SAR images from April and October 1994 (SRL-1 and SRL-2), acquired over the high Alpine test site O¨tztal in Austria. The environmental conditions were different at the time of the three experiments. Ground measurements, meteorological observations, and backscattering modeling are the basis for interpreting the backscattering signatures. Seasonal differences are due mainly to the presence or absence of snow and due to changes of its properties. Short term variations of snow conditions can be monitored at C- and X-band. For unglaciated areas, the surface roughness has a dominant influence on backscattering in all seasons. The dependence of the mean backscattering and correlation coefficients on the incidence angle was analyzed. Spectral and depolarization ratios and the magnitude of the HHVV correlation coefficient were selected as components of the multidimensional feature vector for studying the target separability. Good separability was found between the accumulation and ablation areas on the glaciers, whereas on ice-free areas, the dominance of surface roughness limits the discrimination of different surface types. Short-term variations of backscattering have significant impact for the classification of accumulation and ablation areas on glaciers, as verified by comparisons with field data