Sea ice classification using SAR backscatter statistics

Sea ice classification accuracy using standard statistics and higher order texture statistics generated from grey-level co-occurrence (GLC) matrices were compared for synthetic aperture radar (SAR) data collected during the Marginal Ice Zone Experiment (MIZEX) in April 1987. Standard stepwise discriminate analysis was used to identify the statistics modes useful for discrimination. Range was the most effective statistic, correctly classifying the ice types 75% of the time. Overall, the standard statistics (mean, variance, range, etc.) outperformed the texture statistics (87% accuracy vs. 75% accuracy). Given the added difficulty and computational cost of generating texture statistics, this result suggests that standard statistics should be used for sea ice classification. Odden and multiyear ice categories were the most difficult to statistically separate for these data     

Multisensor approach to automated classification of sea ice image data

A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification.     

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     

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.     

A wavelet transform coder supporting browsing and transmission of sea ice SAR imagery

A wavelet-transform-based algorithm for sea ice synthetic aperture radar (SAR) image data compression is presented. Compression of the relatively low resolution (100 m) SAR data is necessary to enable the transmission of such images from the Finnish Ice Service to users in the Baltic Sea. On board the ships, the SAR images are used for navigation purposes. Hence, in the visual appearance of the compressed image, special attention must be paid to the sharpness of small details. Several target-dependent features are incorporated in the compression scheme developed. The addition of these features was motivated by an examination of the wavelet coefficient statistics for the SAR data. In the quantization phase, the sensitivity characteristics of the human visual system were taken into account. As a result, the proposed algorithm deviates in several respects from the standard procedures used in wavelet-based compression. The algorithm requires the setting up of multiple user-defined parameters to satisfy the requirements of the users and data. This requires supervision of an expert, but also makes it flexible for many kinds of data and user requirements. The algorithm presented gives satisfactory results with a compression ratio of 20:1. Since only the location of ice-covered areas is of significance for ship traffic, the algorithm introduced contains an option to mask off open sea areas with the aid of an automatic open-water detection procedure. We also report the results of a user evaluation, in which the proposed algorithm is compared to the algorithm currently used by the Finnish Ice Service, as well as to the JPEG standard. The results of the evaluation favor the use of the proposed algorithm.     

Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.     

HF ground wave propagation over sea ice for a spherical Earth model

Calculations of the field strength at 10 MHz are reported for a ground wave signal where the path is sea water covered by a uniform layer of sea ice. It is shown that a surface wave type mode may dominate the conventional ground wave modes at the shorter ranges (i.e., < 10 km). At larger ranges the field strength due to the ice layer may be seriously degraded.     

Considerations for SAR image quantification unique to orbitalsystems

A discussion is presented of quantitative expressions required for the response of a synthetic aperture radar (SAR) to both point and distributed scatterers for purposes such as calibration and polarimetry. Image gains depend on the viewing geometry, which is unlike the flat Earth case, which often is assumed to apply in an orbital geometry. Image signal-to-noise ratio is dependent on footprint velocity, but the mean clutter-to-noise ratio for distributed scatterers is dependent on spacecraft velocity. When imagery of a distributed scene observed by an orbital SAR is to be calibrated by comparison to the impulse response of a reference point scatterer, for example, the velocity ratio enters the expression for peak power, but does not enter when an integral is used over the impulse response. The author also looks at the processing gain resulting from overlapping image pixels in azimuth through sampling of the pulse repetition frequency     

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     

A multi-scale morphological approach to SAR image edge detection

This paper introduces a multi-scale morphological edge detection algorithm to extract SAR image edge which suffers seriously from noise. Combining the basic theme of morphology with that of multi-scale analysis, the algorithm presents the outstanding characteristics of accuracy and robustness. Comparative Experiments reveal its fine performance.     

稀疏表示分类在SAR图像目标识别中的应用分析

文中研究了稀疏表示分类在合成孔径雷达(SAR)目标识别中的应用。稀疏表示分类是基于压缩感知理论的一种新的分类算法,近年来在人脸识别、遥感图像分类等领域得到广泛应用。文中对稀疏表示分类在SAR目标识别中的应用进行分析研究,介绍了稀疏表示的基本原理以及几种典型的稀疏系数求解算法。采用稀疏表示分类器对MSTAR数据集进行了目标识别实验,验证在SAR目标识别上的性能。     

最大均匀区域滤波器在SAR图象处理中的应用

介绍了最大均匀区域滤波器的基本原理,为获得预期的滤波效果,在实现过程中,采用模拟匹配的方法进行阶跃边缘的检测,使窗口移动的方向更易确定;用聚类分析与统计特征参数相结合的方法,建立不同灰度区域的判别门限;使门限值更加准确;通过重新估计噪声的相对标准差,对局部滤波算法进行了改进,实验结果表明,这些方法和措施是有效的。     

A novel lacunarity estimation method applied to SAR image segmentation

Based on the relative differential box-counting algorithm and the gliding-box algorithm, a novel method for estimating the lacunarity features of grayscale digital images is proposed. Four natural texture images are used to test the performance of the novel lacunarity measure. Comparisons with published methods show that the proposed method can efficiently describe texture images, and provide accurate classification results. Real synthetic aperture radar (SAR) images analyses are found to have different lacunarity values for different regions. We show that good results can be obtained with appropriate lacunarity parameters applied to SAR images segmentation.     

A comprehensive, automated approach to determining sea icethickness from SAR data

Documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal changes and is not subject to limitations caused by using predefined parameters     

复轮廓波包变换及其在SAR图像去斑中的应用

利用解析的双树复小波包变换(DT-CWPT)与非抽样方向滤波器组(NSDFB),构造了一种复轮廓波包变换(CCPT),并将其应用于合成孔径雷达(SAR)图像去斑。新的变换由于对信号的低频和高频部分都进行了分解,因此除了具有多分辨率、局部性、方向性和各向异性的特点外,还具有平移不变性和更丰富的方向分量。实验结果表明,本文构造的CCPT能够有效地抑制斑点噪声,达到较好的边缘保持效果,并且在图像的细节和纹理表现方面具有一定的优势。     

Fade duration statistics ofL-band multipath fading due to sea surface reflection

Multipath fading in maritime satellite communications may be caused by a combination of sea surface reflection and tropospheric effects such as ducting and scintillations. Among them, multipath fading due to sea reflection is dominant when receiving satellite signals by use of a wide beamwidth antenna. In this paper, fade duration statistics of multipath fading due to sea surface reflection are analyzed using the data obtained by on-board experiments on theL-band (1.54 GHz). Results indicate that the mean value of fade duration and fade occurrence interval can be determined from the fading power spectrum with fairly good precision, and that the probability density function for a given time percentage, ranging from 50 to 99 percent, approximates well with the exponential distribution. Finally, based on the results obtained, a simple method for estimating the mean values of fade duration and fade occurrence interval is presented.     

基于Canny-ROA算子和Hough变换的SAR图像道路检测

提出了一种高分辨率SAR图像的道路提取算法。首先对SAR图像进行FROST滤波,去除相干斑噪声,再融合Canny算子和ROA算子提取边缘点,并对图像边沿的信息进行补偿,然后使用平行线滤波滤除相关地物对道路检测的影响,最后利用Hough变换提取线特征,并对断裂的直线进行连接。实验结果表明,该方法能有效滤除护栏等地物的影响,对直线道路具有良好的检测效果。     

A review of sea ice and ocean modeling relevant to the Labrador andNewfoundland shelves

Numerical and analytical models of sea ice and ocean circulation are reviewed from a viewpoint of their application to the Labrador and Newfoundland shelves. Seasonal variabilities of sea ice and ocean circulation have not been reported in numerical models. The difficulty comes from the importance of both stratification and bottom topography. Sea ice should be included for seasonal cycle calculations. Individual mechanisms of variabilities with time scales ranging from a day to a month have been studied extensively using various models so that the coupling of these models will make it possible to test the predictability of sea ice and ocean circulation. The data collected during LIMEX'87 and '89 provide verification of the models