Polarimetric Characteristics of sea ice in the sea of Okhotsk observed by airborne L-band SAR

The Phased-Array L-Band SAR (PALSAR) aboard the Advanced Land Observing Satellite (ALOS) is capable of globally acquiring fully polarimetric data. In order to confirm the ability of L-band polarimetric synthetic aperture radar (SAR) to investigate sea ice before the ALOS launch, we conducted a field experiment using an airborne Polarimetric and Interferometric SAR (Pi-SAR) in the Sea of Okhotsk in 1999. This paper presents the analyzed results of data acquired in that experiment. The extracted polarimetric parameters of several ice types suggested that polarimetric coherences and phase differences between right-right (RR) and left-left (LL) are good candidates for discriminating ice types. The polarimetric anisotropy as well as the beta angle of the first eigenvector calculated in the polarimetric decomposition procedure are alternative parameters that are sensitive to ice type differences. Due to the low depolarization characteristics of open water, it could be discriminated from sea ice by scattering entropy in all incidence angle ranges. From the relation between ice thickness and the polarimetric parameters, we found that backscattering coefficients and vertical (VV) to horizontal (HH) backscattering ratio are highly correlated with ice thickness. Since the ratio is sensitive to ice surface dielectric constants, a simple simulation using the integral equation method surface model was conducted by using the physical parameters of typical sea ice. A two-dimensional ice thickness map was derived from an empirical relation between the VV-to-HH backscattering ratio and ice thickness.     

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     

双波段全极化SAR图像非监督分类方法及实验研究

该文首先采用H/分类对像素进行了初始猜测,然后进一步采用Bayes最大似然估计(ML）分类法对像素进行重新归类．不同波段电磁波对地物散射具有不同的属性,因而我们采用双波段全极化SAR数据结合的分类方法,得到了更好的分类结果．SAR图像的相干斑会影响图像的分类准确度和精度．在进行分类处理前,对双波段全极化SAR图像相干斑进行矢量滤波处理．该文使用NASA/JPL实验室在天山地区的实测数据对这些分类算法进行了实验研究．给出了单波段以及双波段全极化SAR分类结果的伪彩色图．其中双波段全极化SAR滤波后数据具有相对最优的分类结果．     

Universal multifractal scaling of synthetic aperture radar imagesof sea-ice

Multifrequency, multipolarization imaging radar scattering coefficient data sets, acquired by synthetic aperture radar (SAR) over sea-ice, were studied in order to reveal their scale-invariant properties. Two distinct scenes were acquired at C-band (5.6 cm) and L-band (25 cm) wavelengths for three different linear polarizations (HH, VV, and HV). These sea-ice radar scattering coefficient fields were investigated by applying both Fourier and multifractal analysis techniques. The (multi) scaling of the data is clearly exhibited in both scenes for all three polarizations at L-band and for the HV polarization at C-band. The fields presenting this symmetry were found to be well described by universal multifractals. The corresponding parameters α, C₁, and H were determined for all these fields and were found to vary little with only the parameter H (characterizing the degree of nonconservation) displaying some systematic sensitivity to polarization. The values found for the universal multifractal parameters are α≈1.85±0.05, C₁≈0.0086±0.0041, and H≈-0.15±0.05     

On the dimensionality of multiparameter microwave image data fromthin sea ice in the Labrador Sea

Thin ice is typically defined as comprising the World Meteorological Organization's young and new ice categories, referring to sea ice that is less than 0.3 m thick. This ice type is an extremely important factor in both the thermodynamic and dynamic properties of both polar and marginal ice covers. In the LIMEX'89 experiment, spatially and temporally registered multipolarization data from 5.3 and 9.25 GHz SARs and from 37 and 90 GHz imaging radiometers were acquired over a region containing a wide range of new ice growth stages in the Labrador ice pack. The temperatures at the data acquisition time were -10 and correspond to ice growth conditions. In this paper, the dimensionality of the multifrequency, multipolarization active and passive data set is examined to determine the complementarity of the sensor parameters and sensor types for thin ice measurements. Principal component analysis is used to provide estimates of the information content of individual measurement channels and their combinations. Various measurement subspaces are examined. Criteria for channel redundance are proposed and tested and the classification potential of the multidimensional measurement set is tested for thin ice growth stages that are known to present classification difficulties for microwave sensors. Given six nominally independent SAR measurement channels, the information space dimension of this subspace is shown to be greater than five. The four radiometer channels are shown to have information space dimension two and are redundant in frequency. The combined, ten element, SAR/radiometer measurement space was shown to have information space dimension eight under the criteria used when the scattering polarization ratios are included     

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     

Classification of multifrequency polarimetric SAR imagery using adynamic learning neural network

A practical method for extracting microwave backscatter for terrain-cover classification is presented. The test data are multifrequency (P, L, C bands) polarimetric SAR data acquired by JPL over an agricultural area called “Flevoland”. The terrain covers include forest, water, bare soil, grass, and eight other types of crops. The radar response of crop types to frequency and polarization states were analyzed for classification based on three configurations: 1) multifrequency and single-polarization images; 2) single-frequency and multipolarization images; and 3) multifrequency and multipolarization images. A recently developed dynamic learning neural network was adopted as the classifier. Results show that using partial information, P-band multipolarization images and multiband hh polarization images have better classification accuracy, while with a full configuration, namely, multiband and multipolarization, gives the best discrimination capability. The overall accuracy using the proposed method can be as high as 95% with a total of thirteen cover types classified. Further reduction of the data volume by means of correlation analysis was conducted to single out the minimum data channels required. It was found that this method efficiently reduces the data volume while retaining highly acceptable classification accuracy     

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     

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     

Potential Application of Multipolarization SAR for Pine-Plantation Biomass Estimation

This paper presents the techniques and the potential utility of multipolarization Synthetic Aperture Radar (SAR) data for pineplantation biomass estimation. Three channels of SAR data, one from the Shuttle Imaging Radar SIR-A and the other two from the aircraft SAR, were acquired over the Baldwin County, Alabama, study area. The SIR-A data were acquired with HH polarization and the aircraft SAR data with VV and VH polarizations. Linear regression techniques are used to estimate the pine-plantation biomass, tree height, and age using 21 test plots. The results indicate that the multipolarization data are highly related to the plantation biomass. The results suggest a potential application of multipolarization SAR for pine-plantation biomass estimation.     

Segmentation of polarimetric synthetic aperture radar data

A statistical image model is proposed for segmenting polarimetric synthetic aperture radar (SAR) data into regions of homogeneous and similar polarimetric backscatter characteristics. A model for the conditional distribution of the polarimetric complex data is combined with a Markov random field representation for the distribution of the region labels to obtain the posterior distribution. Optimal region labeling of the data is then defined as maximizing the posterior distribution of the region labels given the polarimetric SAR complex data (maximum a posteriori (MAP) estimate). Two procedures for selecting the characteristics of the regions are then discussed. Results using real multilook polarimetric SAR complex data are given to illustrate the potential of the two selection procedures and evaluate the performance of the MAP segmentation technique. It is also shown that dual polarization SAR data can yield segmentation resultS similar to those obtained with fully polarimetric SAR data     

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.     

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     

Polarimetric calibration of the SIR-C C-band channel using activeradar calibrators and polarization selective dihedrals

A polarimetric calibration experiment of Shuttle Imaging Radar C (SIR-C) is carried out using several different calibration targets. These are C-band polarimetric active radar calibrators (PARCs), polarization selective dihedrals (PSDs), 22.5° rotated dihedrals, and a trihedral. A novel polarimetric calibration algorithm is proposed that combines existing algorithms and uses one PARC and two PSDs. An error evaluation example is shown to estimate the typical hardware error value of the calibration targets allowable for a given calibration error. The novel algorithm gives polarimetric calibration results comparable to those obtained using the existing algorithm for three PARCs. Since PSDs work at frequencies lower than design frequency, and hence can be commonly used at multiple frequency bands, the simple addition of just one more frequency band PARC allows polarimetric calibration of a dual-frequency polarimetric synthetic aperture radar (SAR) by means of the proposed algorithm     

Snow mapping in alpine regions with synthetic aperture radar

Active microwave sensors can discriminate snow from other surfaces in all weather conditions, and their spatial resolution is compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR in the Otztal Alps in 1989 and 1991, the authors examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. By comparing polarimetric SAR data to images from the Landsat Thematic Mapper obtained under clear conditions one week after the SAR flight, the authors found that SAR data at 5.3 GHz (C-band) can discriminate between areas covered by snow from those that are ice-free. However, they are less suited to discrimination of glacier ice from snow and rock. The overall pixel-by-pixel accuracies-74% from VV polarization alone with topographic information, 76% from polarimetric SAR without any topographic information, and 79% from polarimetric SAR with topographic information-are high enough to justify the use of SAR as the data source in areas that are too cloud-covered to obtain data from the Thematic Mapper. This is especially true for snow discrimination, where accuracies exceed 80%, because mapping of a transient snow cover during a cloudy melt season is often difficult with an optical sensor. The AIRSAR survey was carried out in summer during a heavy rainstorm, when the snow surfaces were unusually rough. Even better results for snow discrimination can be expected for mapping in the spring, when snow is usually smoother     

Filament Preserving Model (FPM) Segmentation Applied to SAR Sea-Ice Imagery

Modeling spatial context constraints using a Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to synthetic aperture radar (SAR) sea-ice segmentation has also been demonstrated recently. However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and necessary for identifying certain ice types. In this paper, a new statistical context model is proposed that, within the same scene, can simultaneously preserve narrow elongated features while producing similar smooth segmentation results comparable to typical MRF-based approaches. Tested on one synthetic image and two SAR sea-ice scenes, this filament preserving model substantially improves classification accuracies when compared to standard Gaussian mixture and MRF-based segmentation algorithms     

Airborne C-band SAR measurements of wet snow-covered areas

Using polarimetric synthetic aperture radar (SAR) data at C-band, the optimum polarization and range incidence angles for the classification of land covers found in the James Bay area, P.Q., Canada-open areas, lakes ice, and forests, all covered with wet snow-have been examined. The separability between classes shows that they can be classified by a single polarization. For &thetas;<30°, the overall classification accuracies were 97.1, 98, and 90.8% from HH-, cross, and VV-polarizations alone, respectively. They were greater than 99% for all polarizations at &thetas;>30°. However, the polarimetric parameters were not suitable for classification     

C-Band Scatterometer Measurements of Multiyear Sea Ice Before Fall Freeze-Up in the Canadian Arctic

Backscatter signatures of multiyear sea ice (MYI) during the late summer and early fall season before the fall freeze-up in the Canadian Arctic Archipelago (CAA) have been obtained through the use of a ship-based polarimetric scatterometer. The device operates in C-band, and measurements were conducted in swaths from incidence angles of 20 $^{circ}$–60$^{circ}$ . Three characteristic sites on MYI floes were investigated in the high Arctic and the central Arctic regions. In situ snow and sea-ice thermophysical data were collected at each site in conjunction with local scatterometer measurements. The thermophysical data were subsequently analyzed using dielectric modeling techniques and coupled with the backscattering measurements $(sigma^{circ})$. Observed backscatter values and ratios were found to be in agreement with literature data, with volumetric scattering as the dominant scattering mechanism.      

Multipolarization SAR Data for Surface Feature Delineation and Forest Vegetation Characterization

This paper presents the utility of multipolarization Synthetic Aperture Radar (SAR) data for surface feature delineation and forest vegetation characterization. Three channels of ratioed data (VV/HH, VH/HH, and VH/VV) are generated from the HH, VV, and VH polarization data (V = vertical, H = horizontal). The ratioed data are linearly stretched to yield a digital number within a range of 0 to 255. The techniques for reducing SAR speckle noise and for measuring the degree of separation are discussed. For surface feature delineation, the results indicate that cross polarization as well as cross polarization ratioed data better delineate those surface features that are difficult to separate by like polarization data. The results suggest using a median value filtering technique to reduce within-plot data fluctuation to increase the separability measure. For forest vegetation characterization, the results indicate that multipolarization SAR data may be used to estimate forest properties such as total-tree biomass, basal area, and tree height.     

Polarimetric SAR calibration experiment using active radarcalibrators

Active radar calibrators are used to derive both the amplitude and phase characteristics of a multichannel polarimetric synthetic aperture radar (SAR) from the complex image data. Results are presented from an experiment carried out using the NASA/JPL DC-8 aircraft SAR over a calibration site at Goldstone, California. As part of the experiment, polarimetric active radar calibrators (PARCs) with adjustable polarization signatures were deployed. Experimental results demonstrate that the PARCs can be used to calibrate polarimetric SAR images successfully. Restrictions on the application of the PARC calibration procedure are discussed