Interpretation of Seasat radar-altimeter data over sea ice using near-simultaneous SAR imagery

Abstract

The backscatter properties of Seasat altimeter data in the Beaufort Sea on the 3 October 1978 show distinct zones, which arc interpreted in terms of geophysical characteristics. An overlapping and near-simultaneous synthetic-aperture radar image shows regions of open water, new ice and multi-year sea ice, which correspond to the different zones. It is found that the altimeter signal is sensitive to the ocean-ice boundary and that it indicates the ice type. The pulse-echo waveforms also suggest that several scattering components are present in the returned power over sea ice.     

Classification of sea ice types in the Ross Sea,Antarctica from SAR and AVHRR imagery

ERS-2 synthetic aperture radar (SAR) and Advanced Very High Resolution Radiometer (AVHRR) imagery are used to examine spectral characteristics of late winter/early spring ice in the Ross Sea, Antarctica. The combined spectral signatures are used to distinguish six ice types: fast ice, new ice, smooth first year ice, rough first year ice, thin new ice/wind roughened open water and glacial ice. The procedure firstly involves 'picking' class boundaries from SAR imagery based on the morphology of a speckle reduced backscatter spectrum. These class boundaries are then used as input to an iterative segmentation procedure that involves the repeated application of a speckle reduction filter to the image. For an image from late September 1996 the segmentation procedure enabled separation of five general ice categories each with a characteristic backscatter range. However because of the combined contributions of ice thickness, surface roughness, salinity and water content to the SAR backscatter, further decision criteria are required to separate some physical ice types unable to be resolved individually using this method. Coincident and co-registered infrared data from the AVHRR sensor are used to extract spectral characteristics for the final ice classes. Using this procedure we were able to distinguish floating glacier ice from thin new ice/wind roughened open water and new ice from nearshore fast ice. These ice types were unable to be separated using SAR backscatter intensity alone. In addition image subtraction was also able to clearly delineate areas of shore fast ice.     

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。     

Mapping ice sheet margins from ERS-1 SAR and SPOT imagery

We demonstrate a new technique to automatically map the margin of ice sheets using ERS-1 SAR and SPOT imagery. The technique relies on a series of image processing techniques including edge enhancement, dynamic thresholding, region growing, edge detection, and edge following. The mapping technique is used to measure temporal displacement of the ice sheet margin north and south of the Jakobshavn Glacier in Greenland. During the 4 year interval between 1988 and 1992 we find ice sheet margin advance/retreat rates of about +/- 12m/year, consistent with independent observation. We combine the 1988 and 1992 data to assign an average ice margin location for the observation interval assuming a constant displacement rate. The resulting snapshot of the ice sheet margin can be used as a benchmark for future change detection studies. In both cases the uncertainties are better than 200m.     

Semi-automated feature-tracking of East Antarctic sea ice from Envisat ASAR imagery

While feature tracking of sea ice using cross-correlation methods on pairs of satellite Synthetic Aperture Radar (SAR) images has been extensively carried out in the Arctic, this is not the case in the Antarctic. This is due to the dynamic nature of Antarctic pack ice, its microwave signature, the tendency for SAR swath paths to be poorly aligned with the often narrow sea ice zone around the continent and inadequate satellite sampling. A semi-automated system, known as IPADS (IMCORR [IMageCORRelation] Processing, Analysis and Display System), has been developed to map fast ice and pack ice in Antarctica using multiple pairs of SAR images. The software processing pipeline uses overlapping image pairs which are geocoded and roughly registered using only data contained in the image headers. Next, fast ice maps are rapidly generated using zero motion features located within ocean regions. This also provides precise image registration. Finally, the same image pairs are re-examined for pack ice motion in a slower off-line batch process. The pack and fast ice are identified using a cluster-based search method which compares both location and motion information. Each image pair generates a NetCDF file which adds to a growing database of Antarctic sea ice motion and ice roughness. Five image-pair examples are presented to illustrate the methods used as well as their strengths and limitations. Substantial pack ice motion can often be detected in the marginal ice zone on SAR images only a few days apart.     

Segmentation of Synthetic-Aperture Radar imagery of sea ice

This paper describes a ‘segmentation’ algorithm, which has been designed for Synthetic-Aperture Radar (SAR) sea-ice images as part of the U.K. Earth Observation Data Centre (EODC) Science Team activity. The segmented sea-ice product consists of distinct, statistically-homogeneous regions of ice and water from which various atttributes can be determined, such as area, centroid position, mean intensity and standard deviation, shape, orientation and texture. Such attributes are important in providing a template from which it is possible to derive information such as ice type, concentration and dynamics, the position of the ice edge, and the characteristics of leads and polynyas. Although the method has been developed for processing ERS-1 data, it is demonstrated in this work using both simulations and SAR sea-ice data obtained from the Seasat campaign of 1978, as ERS-1 data were not yet available at the t ime of implementation.     

Discriminating sea ice from low-level water clouds in split-window,mid-wavelength IR imagery

A technique is demonstrated to enhance the contrast between sea ice and low-level water clouds. The approach uses the brightness temperature difference (BTD) feature from data collected in the split-window, mid-wavelength infrared (IR) region (i.e. two bands at 3.7 μm and 4.0 μm). These spectral data are available with Visible Infrared Imager Radiometer Suite (VIIRS) moderate-resolution bands M12 and M13, respectively. Under daytime conditions, the data collected in these bands contain energy that originates from both the sun and the Earth–atmosphere system. Due to the small wavelength difference between these, the terrestrial energy component in the bands is typically quite similar as are the surface reflectances for sea ice and ocean surfaces. Thus, the enhanced contrast between sea ice and water clouds, evident in a M12–M13 BTD image, results from differences in the solar energy, which decreases rapidly across this atmospheric window. Observed BTD values for water clouds can exceed 30K, while those for snow-ice fields are typically much smaller (e.g. 0–5K). Thus, water clouds appear bright in the image while sea ice, oceans, and most land surfaces are very dark. The enhanced contrast in the split-window, mid-wave IR BTD image makes it valuable for both image analysis and use in cloud algorithms. In addition, these images support the creation of manually generated cloud masks that have been shown useful for quantitatively evaluating the performance of automated cloud analysis algorithms and cloud forecast models. In this article, the value of 3.7 μm minus 4.0 μm BTD imagery for distinguishing between sea ice and low-level water clouds is shown using VIIRS data collected over the Beaufort Sea on 31 May 2012. Manually generated cloud masks, derived in part from these data, are then used to quantitatively evaluate the effectiveness of various cloud tests, including those used in the VIIRS cloud mask algorithm and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm. The results strongly suggest that split-window, mid-wavelength IR imagery provides valuable information to help differentiate between clouds and sea ice. It is concluded that collecting data in these mid-wavelength IR bands should be considered part of any future satellite sensor designed for environmental monitoring, especially over the polar regions.     

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。     

Real-time use of ERS-l SAR imagery for ice service and ice breaking operations in the Baltic Sea

Sea ice forms every winter in the Baltic Sea and several icebreakers in Sweden and Finland keep the major ports in the area open for sea-trade all the year around. Information and forecasts of the sea ice formation, drift and deformation are vital for safe and efficient winter navigation. In this respect, Synthetic Aperture Radar (SAR) imagery is of great interest, since this technique is almost cloud independent and has potential for real-time ice mapping. The usefulness of SAR imagery for sea ice operations has been evaluated in the Baltic Sea. The imagery was used both for ice mapping, for icebreaker operations and ship routeing. Images presented onboard the icebreakers were highly appreciated and easily interpreted by the crew. The data were frequently used for ship routeing (33 per cent) of merchant vessels and for direct icebreaker assistance (53 per cent). It was concluded by several icebreaker masters that an image resolution of about 100m was indeed enough to distinguish ridged areas and in the same time obtain a large enough geographical coverage per image.     

Characterization of the coherent scattering induced by ridging patterns in agriculture by the use of polarimetric SAR imagery

This article investigates the scattering characteristics of ridging patterns in agriculture by the use of C-band polarimetric synthetic aperture radar (PolSAR) images. The polarimetric signatures of periodic potato fields and row wheat in different directions are highlighted using a set of polarimetric parameters. Enhanced coherent scattering is observed when the alignment direction of the ridging patterns is perpendicular to the radar’s line of sight (LOS). The dominant backscattering mechanism of the ridging patterns is deduced by evaluating different polarimetric parameters. The increased copolarized backscattering coefficients and copolarized correlation coefficient, and the reduced entropy and polarimetric alpha angle, indicate a strong contribution of odd scattering to ridging patterns aligned perpendicular to the LOS. We also compare the dominant contributions to the backscattering of ridging patterns in different phenological stages. Although the canopy changes of potato and wheat with time are significant, the underlying periodic surface changes the dominant scattering mechanism of potato fields over all the phenological stages, and the wheat aligned parallel with the flight direction of radar still has relatively high coherent scattering in the different vegetation development stages. The variability analyses undertake in this study allow a more detailed documentation of the physical scattering process of the ridging patterns in agriculture, and will improve the applicability of synthetic aperture radar images in agriculture.     

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.     

Antarctic ice sheet and sea ice regional albedo and temperature change, 1981-2000, from AVHRR Polar Pathfinder data

Spring-summer (November, December, January) ice sheet and sea ice regional surface albedo, surface temperature, sea ice concentration and sea ice extent averages and trends from 1981 to 2000 have been calculated for the Antarctic area. In this research the AVHRR Polar Pathfinder 5-km EASE-Grid Composites and the combined SMMR and SSMI data sets from the National Snow and Ice Data Center (NSIDC), Boulder, Colorado have been employed. A regional analysis has been made for five longitudinal sectors around Antarctica: the Weddell Sea (WS), the Indian Ocean (IO), the Pacific Ocean (PO), the Ross Sea (RS) and the Bellingshausen-Amundsen Sea (BS). The IO and PO sectors show ice sheet albedos of 0.85 and temperatures of − 25 °C. The corresponding values in the RS and BS sectors are 0.80 and − 16 °C respectively. The sea ice albedo is about 0.60 in the RS, BS and WS sectors and 0.55 in the IO and PO sectors. The average sea ice temperature varies around − 12 °C. All the sectors show slight increasing spring-summer albedo trends and decreasing spring-summer temperature trends and similar interannual variability in albedo and surface temperature. The steepest ice sheet albedo trend of 0.0019 ± 0.0009/yr is found in the RS sector. The steepest sea ice albedo trend of 0.0044 ± 0.0017 /yr occurs in the PO sector. The steepest temperature trends for both the ice sheet and sea ice occur in the BS sector, having values of − 0.075 ± 0.040 °C/yr and − 0.107 ± 0.027 °C/yr respectively. The sea ice concentration shows slight increasing trends, the highest being in the PO sector (0.3 ± 0.12%/yr), whereas the sea ice extent trends are near zero with the exception of the RS sector (14,700 ±8600 km²/yr) and the BS sector (− 13,000 ± 6400 km²/yr).     

Airborne line scanner measurements for ERS-1 SAR interpretation of sea ice

A new method is presented for interpretation of Synthetic Aperture Radar (SAR) images recorded with the first European Remote Sensing Satellite (ERS-1) with respect to classification of sea ice. Conventional interpretation of spaceborne radar images is often based on subjective classification of radar signatures. Themost promising approach is a region-based segmentation with subsequent interpretation of the segments. The definition of regions with common properties enables a characterisation by texture parameters besides backscatter coefficients. The major drawback of this method is the subjective selection and characterisation of the training segments on which the classification is based. To avoid this subjective influence, an attempt is made of an independent classification by the use of two airborne line scanner systems, one for the visible and the other for the infrared spectral range. The systems were installed on research aircraft (aeroplane and helicopter) during an experiment covering the marginal ice zone north of Spitsbergen in late winter 1993 and in the North East Water Polynya in summer 1993. Several underflights of ERS-1 were carried out during the SAR mode. Typical areal coverage of the line scanner images during a flight is 1.5% of a SAR scene. The line scanner images are classified using a two-dimensional feature space. By this procedure various ice types are identified. After matching the locations of the line scanner and the segmented SAR images, it is then possible to derive the characteristics of different ice types with respect to radar signatures. Backscatter coefficient and texture parameters are used for the discrimination of ice types. Texture parameters used are moments of the grey-level distribution and values of the co-occurrence matrices. The results of this work show, that the common method of a subjective characterisation of training regions runs into difficulties. SAR signatures are essentially dependent on the conditions during ice development. Regions with different ice types can have the same SAR signatures and different SAR signatures may be found for the same ice type. With the presented method it is possible to achieve classification accuracies up to 78% with a discrimination of young ice, first-year ice and old ice during winter situations. In summer, it is difficult to discriminate wind exposed water and sea ice. In this case, the classification of SAR images is derived exclusively by means of texture parameters since the backscatter coefficient is of little use. With the discrimination of water, snow-covered ice, and brash ice an accuracy of 73% is achieved under summer conditions.     

极化SAR影像边缘检测综述

极化合成孔径雷达（SAR）图像包含目标丰富的散射信息,在边缘检测中具有重大的潜力。对极化SAR影像边缘检测问题进行了系统的研究,从单极化SAR出发,分析了极化SAR边缘检测问题,对已有的方法进行了分类总结,重点介绍了极化SAR边缘检测的最新进展,指出了当前存在的问题,对极化SAR边缘检测的发展趋势进行了展望。     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

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.     

Unsupervised urban area extraction from SAR imagery using GMRF

This paper presents a new method for unsupervised urban area extraction from SAR imagery using two different GMRF models. One model is the T-based GMRF model proposed by Xavier Descombes specially for acquiring urban area in panchromatic SPOT imagery. When it is used for urban area extraction from SAR imagery, some missing detection occurs. The other model is the conventional GMRF model that requires training samples for urban area extraction. When it is used for SAR imagery, the extraction result includes all urban areas and some false detection. Three steps are made up in our method. First, we adopt a threshold for the T-based GMRF model parameter T to acquire the result of urban area extraction. Then, taking the result as training samples, we estimate the conventional GMRF model parameters and acquire a new result of urban area extraction. Finally, we fuse the two results above using a region-growing algorithm to form the final accurate urban area extraction. Experimental results show that the proposed unsupervised approach can obtain accurate urban area delineation. The text was submitted by the authors in English. Yang Yong. Born in 1978. Now a postgraduate in the Department of Communication and Information Systems, School of Electronic Information, Wuhan University. The research direction is Image Processing. Scientific interestsare SAR image segmentation and classification with the Markov random field approach. Hong Sun. Born in 1954. Graduated from the Huazhong University of Science and Technology of Electrical Engineering in 1982. Received a Doctoral degree in 1995. Author of Advanced Digital Signal Processing, which is widely used as a textbook for graduated students in China. Scientific interests include statistical signal processing, image analysis, and communication signal processing. Yongfeng Cao. Born in 1976. Graduated from Wuhan University of China in 1999. Assistant and doctoral candidate in the laboratory of Signal Processing and Modern Communication, School of Electronic Information, Wuhan University, China. Scientific interests include Markov random fields, Watershed transformation, and SAR image interpretation.     

Unsupervised urban area extraction from SAR imagery using GMRF

A new method is proposed to extract urban areas from SAR imagery using two different Gaussian Markov Random Field (GMRF) models. Firstly, by making an initial segmentation by a watershed algorithm, we adopt a particular GMRF model proposed by Descombes et al. (the model is called RGMRF model, distinguished from the conventional GMRF model) to acquire urban areas. In the first model a part of the urban areas from the SAR image is extracted with some missing detection. Then, taking the first result as a training sample, we use the conventional GMRF model to redo the extraction. In the second model a larger area is detected including all urban areas with some false detection. Finally, we fuse the two results using a region-growing algorithm to form the final detected urban area. Experimental results show that the proposed method can obtain accurate urban areas delineation. The text was submitted by the authors in English.     

Speed ambiguity in hurricane wind retrieval from SAR imagery

Under strong ocean surface wind conditions, the normalized radar cross section of synthetic aperture radar (SAR) is dampened at certain incident angles, compared with the signals under moderate winds. This causes a wind speed ambiguity problem in wind speed retrievals from SAR, because two solutions may exist for each backscattered signal. This study shows that the problem is ubiquitous in the images acquired by operational space‐borne SAR sensors. Moreover, the problem is more severe for the near range and range travelling winds. To remove this ambiguity, a method was developed based on characteristics of the hurricane wind structure. A SAR image of Hurricane Rita (2005) was analysed to demonstrate the wind speed ambiguity problem and the method to improve the wind speed retrievals. Our conclusions suggest that a speed ambiguity removal algorithm must be used for wind retrievals from SAR in intense storms and hurricanes.