A method for sub-pixel scale feature-tracking using Radarsat images applied to the Mertz Glacier Tongue, East Antarctica

Tracking features between pairs of satellite synthetic aperture radar (SAR) images using image cross-correlation methods is a powerful tool for defining regions of image movement such as ice flow of Antarctic glaciers. Unlike optical images, processed SAR images have various instrumental effects such as view angle distortions and lateral displacement errors caused by slant range mapping. Co-registration issues are problematic and need to be addressed if there are few/no suitable stationary tie points at or near sea level within the images. Methods are presented that use Radarsat ScanSAR images to reliably measure sub-pixel displacements with errors of ? ± 0.25 pixels but this estimate is target dependent. The techniques and issues are illustrated using examples from a long-term study of the Mertz Glacier Tongue in East Antarctica. For image pairs only a few days apart, this extensive structure forms an excellent grid-like test pattern which provides up to 5000 valid correlation matches. Images separated by 100's of days can then be processed in the same way to reveal many subtle and some large changes over time. The technique described is robust and may be widely applicable.     

混合本征模型的多视SAR影像海冰密度检测

目的 SAR影像中像素光谱测度的空间相关性蕴含着海洋表面和海冰更加丰富的空间特性及其变化信息,因此合理建模这种相关性是高分辨率SAR影像海冰精准解译的关键。提出一种利用随机模型及空间统计学测度刻画海冰空间结构的方法。方法 本文首先,在空间统计学框架下,SAR影像被表示为多值Gamma模型和泊松线Mosaic模型线性加权构建的混合模型,其中多值Gamma模型用于描述海洋表面雷达信号背向散射变化的连续性,而泊松线Mosaic模型则用于表征不同类型海冰表面雷达信号背向散射变化的区域性。利用上述混合模型的一阶、二阶变异函数,建模蕴含在SAR影像中海冰空间结构的变化。结果 对RADARSAT-1影像海冰结构建模并反演其密度。实验区域真实海冰密度分别为20%,80%等,运用本文方法反演所得海冰密度与真实海冰密度误差正负不超过10%。结论 本文提出混合本征模型用以刻画SAR强度影像中海冰像素强度变化的空间关系,能够较好地反演Ungava湾海冰密度分布。为利用遥感影像检测空间机构提供一种全新的方法。     

卷积神经网络和深度置信网络在SAR影像冰水分类的性能评估

目的 海冰分类是海冰监测的主要任务之一。目前基于合成孔径雷达SAR影像的海冰分类方法分为两类：一类是基于海冰物理特性与SAR成像特征等进行分类,这需要一定的专业背景;另一类基于传统的图像特征分类,需要人为设计特征,受限于先验知识。近年来深度学习在图像分类和目标识别方面取得了巨大的成功,为了提高海冰分类精度及海冰分类速度,本文尝试将卷积神经网络（CNN）和深度置信网络（DBN）用于海冰的冰水分类,评估不同类型深度学习模型在SAR影像海冰分类方面的性能及其影响因素。方法 首先根据加拿大海冰服务局（CIS）的冰蛋图构建海冰的冰水数据集;然后设计卷积神经网络和深度置信网络的网络架构;最后评估两种模型在不同训练样本尺寸、不同数据集大小和网络层数、不同冰水比例的测试影像以及不同中值滤波窗口的分类性能。结果 两种模型的总体分类准确率达到93%以上,Kappa系数0.8以上,根据分类结果得到的海冰区域密集度与CIS的冰蛋图海冰密集度数据一致。海冰的训练样本尺寸对分类结果影响显著,而训练集大小以及网络层数的影响较小。在本文的实验条件下,CNN和DBN网络的最佳分类样本尺寸分别是16×16像素和32×32像素。结论 利用CNN和DBN模型对SAR影像海冰冰水分类,并进行性能分析。发现深度学习模型用于SAR影像海冰分类具有潜力,与现有的海冰解译图的制作流程和信息量相比,基于深度学习模型的SAR影像海冰分类可以提供更加详细的海冰地理分布信息,并且减小时间和资源成本。     

基于星载SAR的渤海海冰遥感监测系统设计与研究

针对渤海海洋防灾减灾的需要,提出基于星载SAR构建渤海海冰遥感监测系统。分析探讨了系统的数据源选择及工作流程,并就渤海海冰在SAR图像中的响应、海冰信息提取等关键技术进行了讨论。最后描述了该系统的结构设计及运行应用情况,可为今后类似系统的建立提供参考和借鉴。     

Regional Classes of Sea Ice Cover in the East Antarctic Pack Observed from Satellite and In Situ Data during a Winter Time Period

Ice concentration data alone are often of limited use in many process, and modeling studies as different ice règimes of approximately 100% concentration can have significantly different heat flux, albedo, and other surface properties. Current ice concentration algorithms perform poorly in regions of predominantly thin or highly fragmented ice, which constitute a significant proportion of the pack in East Antarctica. The impact of the sea ice cover on high latitude air–sea interactions and marine ecology depends not only on ice extent and concentration but also on the ice-type composition of the pack. An unsupervised ice classification scheme, using data from four channels of the SSM/I, is presented and tested as a means of gaining important additional, complementary information on surface type. Class interpretation is by comparison with AVHRR, ERS-1 SAR, and near-coincident digital aerial photography and in situ data. The classification does a reasonable job at consistently differentiating the large-scale constituent règimes, including the outer marginal ice zone, the interior pack, and a transition zone separating the two. Given the short period of data analyzed, the cluster maps appear to be generally coherent and consistent through time as the pack changes in response to synoptic-scale atmospheric forcing, although the robustness of the technique needs further testing over longer time periods. An observed crossover in the meridional brightness temperature profiles is a dominant and consistent feature which marks the transition from unconsolidated and wet ice in the marginal ice zone to more consolidated ice with a thicker and drier snow cover in the interior pack. Ambiguities occur at the boundaries of some of these règimes due to sensor resolution limitations and the mixing of different ice types and open water. Also, some ice classes (like brash ice) cannot be distinguished from forming pancake ice. Furthermore, although the signature of the inner pack is usually distinct from that of the outer pack, we observed one extraordinary swell propagation event which led to ice fracturing and surface wetting, and significantly altered the surface classification. The results of this multiparameter study underline the importance of using multisensor systems synergistically to improve interpretation of passive microwave data and better characterize the complex Antarctic pack.     

基于边缘保持的区域能量最小化SAR海冰图像分割

提出了一种基于边缘保持的区域能量最小化的SAR海冰图像分割算法。首先对图像进行SRAD滤波,然后进行分水岭初始分割和区域能量最小化分割,从而得到最终分割结果。将该算法用于SAR海冰图像的分割中,实验结果表明,该分割方法有效、准确性好。     

SAR Sea Ice Image Segmentation Method based on Low Rank Sparse Representation and Improved MRF Model

Accurate segmentation of Synthetic Aperture Radar (SAR)images is the premise of interpreting the distribution information of sea ice.However the existing segmentation methodsare seriously interfered by speckle noise,which leads to high segmentation error and low reliability interpreting results.In this paper,a novel sea ice SAR image segmentation method based on low rank sparse representation is proposed,firstly sparse components are extracted from the source image by using robust principal component,and then bilateral filter is used to enhance the image details.Due to the MRF segmentation model based on fixed potential function cannot accurately reflect the relevance between the areas,MRF segmentation model based on interactive potential function is built to segment the sea ice image accurately.A series of Radarsat satellites data are tested to validate performance of the proposed method,the results show that compare with traditional segmentation algorithms,the proposed method algorithm can not only maintain the connectivity of the image better,but also has higher segmentation accuracy.     

Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery

A method to generate high spatio-temporal resolution maps of landfast sea ice from cloud-free MODIS composite imagery is presented. Visible (summertime) and thermal infrared (wintertime) cloud-free 20-day MODIS composite images are used as the basis for these maps, augmented by AMSR-E ASI sea-ice concentration composite images (when MODIS composite image quality is insufficient). The success of this technique is dependent upon efficient cloud removal during the compositing process. Example wintertime maximum (~ 374,000 km²) and summertime minimum (~ 112,000 km²) fast-ice maps for the entire East Antarctic coast are presented. The summertime minimum map provides the first high-resolution indication of multi-year fast-ice extent, which may be used to help assess changes in Antarctic sea-ice volume. The 2σ errors in fast-ice extent are estimated to be ± 2.98% when ≥ 90% of the fast-ice pixels in a 20-day period are classified using the MODIS composite, or ± 8.76 otherwise (when augmenting AMSR-E or the previous/next MODIS composite image is used to classify > 10% of the fast ice). Imperfect composite image quality, caused by persistent cloud, inaccurate cloud masking or a highly dynamic fast-ice edge, was the biggest impediment to automating the fast-ice detection procedure.     

Analysis of sea ice motion and deformation in the marginal ice zone of the Bering Sea using SAR data

This paper presents a study of sea ice motion and deformation in the marginal ice zone (MIZ) of the Bering Sea in the winter season. Segmentation techniques and statistical methods are applied to high‐resolution synthetic aperture radar (SAR) images to derive ice motion and deformation maps. These techniques involve dynamic local thresholding (DLT), which allows separation of sea ice into different classes of thickness and type. Two ice motion characteristics are observed, one consisting of a translation and a rotation at a scale larger than about 10 km day^?1 and the other consisting of field deformations at a spatial scale of less than about 5 km over a 3‐day period. Sea ice deformation rates are calculated, and the divergence and shear feature of the sea ice in different regions identified. Possible causes, associated with wind, wave, current and internal ice forces, for the sea ice motion and deformation are discussed.     

Estimation of Arctic glacier motion with satellite L-band SAR data

Offset fields between pairs of JERS-1 satellite SAR data acquired in winter with 44 days time interval were employed for the estimation of Arctic glacier motion over Svalbard, Novaya Zemlya and Franz-Josef Land. The displacement maps show that the ice caps are divided into a number of clearly defined fast-flowing units with displacement larger than about 6 m in 44 days (i.e. 50 m/year). The estimated error of the JERS-1 offset tracking derived displacement is on the order of 20 m/year. Occasionally, azimuth streaks related to auroral zone ionospheric disturbances were detected and dedicated processing steps were applied to minimize their influence on the estimated motion pattern. Our analysis demonstrated that offset tracking of L-band SAR images is a robust and direct estimation technique of glacier motion. The method is particularly useful when differential SAR interferometry is limited by loss of coherence, i.e. for rapid and incoherent flow and large acquisition time intervals between the two SAR images. The JERS-1 results, obtained using SAR data acquired by a satellite operated until 1998, raise expectations of L-band SAR data from the ALOS satellite launched in early 2006.     

辽东湾海冰类型SAR响应分析

利用2005～2006年冬季辽东湾海冰双极化ENVISAT ASAR影像时间序列,分析了不同极化方式的ASAR影像对辽东湾海冰的探测能力,结果发现交叉极化图像由于其后向散射动态范围小,限制了其在辽东湾海冰分类中的应用。同时利用SAR图像,结合同步TM数据,开展辽东湾海域不同类型海冰的电磁特性响应分析研究,指出SAR能较好识别固定冰、平整冰和碎冰堆积区,但在探测初生冰时并不可靠,其探测结果与海冰生长阶段以及海冰周围环境条件有关,同时由于受分 辨率限制并不能识别莲叶冰等海冰类型。     

卫星遥感海冰监测技术在河北省近海海域的应用

卫星遥感技术已成为海冰监测的重要技术手段之一。介绍了应用EOS系列卫星、HY-1B卫星和HJ-1A/B卫星等遥感数据提取河北省近海海域海冰分布、海冰类型、海冰厚度等海冰信息的方法。重点研究了应用HJ-1A/B卫星遥感数据进行海冰分类,及根据海冰类型与厚度的对应关系估算海冰厚度的方法,并以2013年1月17日的卫星遥感数据为例,提取海冰信息,制作相关海冰专题产品。结果表明:利用多种卫星遥感数据进行海冰监测,既可相互补充又能提高监测效率和准确率,制作更精细化的海冰监测产品,为河北海洋管理部门制定防灾减灾措施提供重要依据。     

Retrieval of sea ice thickness distribution in the seasonal ice zone from airborne L‐band SAR

Although satellite data are known to be useful for obtaining ice thickness distribution for perennial sea ice or in stable thin sea ice areas, their use in the seasonal sea ice zone (SIZ) is still unresolved. In this study, we approached the problem of ice thickness retrieval by using L‐band Synthetic Aperture Radar (SAR). In the SIZ, ice thickness growth is closely related to ridging activity and therefore we expected surface roughness to be correlated to ice thickness. L‐band SAR is suitable for detecting such surface roughness and should be a useful tool for obtaining ice thickness distribution. To verify this correlation, we conducted shipborne electromagnetic (EM) inductive sounding and supersonic profiling observations with an icebreaker, coordinated with airborne L‐band SAR observations in the southern Sea of Okhotsk in February 2005. The surface elevation was estimated by representing the ship's motion with a low‐pass filter. Backscattering coefficients correlated well with ice thickness and surface roughness, defined by the standard deviation of the surface elevation. This result sheds light on the possibility of determining ice thickness distribution in the SIZ.     

基于冰情图的极地SAR海冰图像分割

冰情图在极区安全航行、气候研究等方面具有重要价值,但其存在不能提供像素级的定位信息、对密集度的估计较粗略等缺陷。基于此,提出一种基于冰情图的边缘保持区域型MRF分割方法。依据冰情图从SAR图像中提取子图像,进行SRAD滤波、分水岭初始分割、区域型MRF分割,合并各子图像得到最终分割结果,实现人工解译和计算机解译的结合,得到像素级的结果,具有物体边缘定位准确、分割效率高、可并行化处理等优点。实验结果表明,该方法对极区SAR海冰图像均具有良好的分割效果。     

多时相遥感影像监测南极冰川流速

测定南极冰川的流速，对于研究南极冰雪物质平衡、南极环境变化及其对全球环境的影响和极区航行有重大意义。本论述了利用多时相卫星遥感影像监测冰川变化的原理和方法，通过对英格里特一克里斯泰森海岸不同时期的遥感影像进行几何纠正和配准及叠加处理，测量和计算出极记录冰川的平均流速，并对它们的变化特点进行了初步的分析和评价。     

Some active and passive microwave signatures of Antarctic sea ice from mid-winter to spring 1991

Antarctic sea ice is often covered by a deep snow layer which acts as an emitter and a scatterer to microwave radiation leading to possible misinterpretations of ice signatures, particularly at high frequencies. The algorithms for ice identification, based on the observations of the Special Sensor Microwave Imager, at 19GHz (vertical and horizontal polarizations) and 37Ghz (vertical polarization), have proven to be inefficient for distinguishing new and old ice over the Antarctic Ocean. At an equivalent resolution and analysed on a weekly basis, complementary information can be obtained from active microwave measurements provided, at 5·3GHz (vertical polarization), by the Active Microwave Instrument, the scatterometer of ERS–1. Based on data obtained from the end of August to the end of November 1991, during the austral winter and spring radar backscatter is analysed as a function of the incidence angle. At low incidence angles, the derivative of the backscatter is closely related to the water concentration as derived from passive radiometry, whereas, at high incidence angles, the backscatter is mainly due to ice, as the water contribution is strongly reduced. During the whole period, stable features are apparent on the images obtained from the backscattering coefficients at 50°. On those images, higher values characterize the marginal ice zone, the polynya areas and the advected ice within the Ross Sea. At high incidence angles, the strong signatures of deformed/ rough ice depart significantly from the information classically extracted from the radiometers, the brightness temperatures as well as the derived products, polarization, spectral gradient ratios and concentration. It is therefore possible to classify the Antarctic ice cover into geographical clusters where the active microwave signatures can be attributed. to a peculiar ice type. Though those clusters are not totally identified, their stability and the coherence of their patterns show that they are related to geophysical structures. Four backscatter curves, simulating distinct behaviours over the Antarctic region, are proposed for sea water, marginal ice, first-year ice of the inner part of the pack and multi-year ice.     

Model-based interpretation of ERS-1 SAR images of Arctic sea ice

Interpretation of Synthetic Aperture Radar (SAR) images of sea ice is complex because of the natural variability of sea ice and sensor-induced effects, such as speckle. Most of the research on SAR image interpretation has focused on the winter months and algorithms were developed to classify sea ice successfully under cold conditions. However, interpretation of SAR images during the seasonal transitions has proved difficult due to rapidly changing weather conditions. In this paper we address the application of SAR during the transition from summer to the fall freeze-up. This period is important because it signals the start of significant new ice growth, which affects the air-ocean heat exchange and injects brine into the upper layers of the ocean. We have interpreted SAR images of the sea ice in terms of the basic ice characteristics by using shipborne radar measurements of sea ice during the freeze-up and models derived from these measurements. We have shown that the model-based approach is effective in interpreting SAR images during this seasonal transition. This work also provides the physical mechanisms responsible for the large increase in backscatter observed at the end of the summer melt season.     

High resolution observations of Weddell Sea surface currents using ERS-l SAR sea-ice motion vectors

Two areas of the Weddell Sea, one in the south and one in the west, were chosen for a preliminary investigation of sea-ice motion tracking from ERSt Synthetic Aperture Radar (SAR) images during the Austral summer. Only a small number of images were processed, so a manual tracking method was used. In the 3–day period between SAR images the atmosphere warmed near the surface, which led to significant changes in radar backscatter from, and thus in contrast between, ice floes and the areas between them. It was therefore not always possible to track features from one image to the next. The tracked features were clearly identified in images which were sub-sampled at onesixteenth of the full resolution available. In the southern Weddell Sea images, many large floes were present which allowed a quite detailed pattern of the surface water circulation to be mapped as the ice motion was predominantly forced by the ocean currents during a period of low surface wind speeds. The observed circulation pattern agreed well with previous observations from hydrographic surveys in this area north of the Filchner Ice Shelf. In the western Weddell Sea images good tracers were hard to find, but it was still possible to detect the edge of the western boundary current of the Weddell Gyre. Continuous monitoring of sea-ice motion in these two areas using SAR imagery could be a useful means of detecting changes in surface water flow which may be linked to the rate of formation of Antarctic bottom water.     

Some concepts for precise estimation of deformations/rigid areas in polar pack ice based on time series of ERS-1 SAR images

This paper describes concepts for an algorithm using pairs of satellite SAR imagery to estimate precisely rigid areas and deformations in polar ice fields. The idea is first to produce a sparse sampling of the ice displacement and then identify rigid subsets of this sparse sampling. The algorithm tunes the position parameters of the images using those of these subsets best fitting the model of rigid motion (shift/rotation). Then the algorithm identifies rigid areas again and so on iteratively improving the identification of rigid areas and the relative geographical positioning in the images. The last step is to classify each pixel in the images as included in one of the given rigid areas or, perhaps, not included in one of these areas.     

一种利用HY-2卫星扫描微波辐射计数据反演极地海冰密集度的算法

海洋二号卫星(HY-2)是中国第一颗海洋动力环境监测卫星,如何有效地利用海洋二号卫星扫描辐射计数据监测海冰成为亟待解决的问题。根据海冰与海水辐射特征,提出一种基于多频段双极化反演海冰密集度方法,该方法利用简化的辐射传输模型和地物参考点模拟亮温并逼近观测亮温。该方法与NASA Team 2和ABA算法具有很好的可比性。应用该方法,可以有效利用HY-2卫星反演海冰密集度,监测海冰边缘与面积变化。