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.     

电波传播研究70年

对流层电波传播特性是影响无线电系统性能的重要因素,是电波传播研究的主要领域. 本文系统综述了近60年来中国电波传播研究所在无线电气象、地面无线电业务电波传播、空间无线电业务电波传播、电波传播效应对无线电系统影响评估等对流层电波传播不同方向开展的研究和取得的研究进展. 在此基础上,最后给出了对对流层电波传播研究的展望.     

蒸发波导传播的统计分析

全面介绍了中国电波传播研究所建所60年来,在对流层大气波导超视距电波环境及传播专业领域的研究及其应用情况,主要包括对流层大气波导机理与预测模型研究、大气波导探测感知与预测预报方法、超短波/微波超视距传播算法、无线电信息化系统装备超视距验证评估及信息保障等反映该领域国内相关技术的发展历程和最新研究成果,并结合实际应用需求提出了未来技术的发展趋势.     

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.     

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。     

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。     

SIFT算法在雷达图像匹配中的应用

为了提高合成孔径雷达（SAR）图像匹配的稳定性和可靠性,提出了一种基于SIFT特征的雷达图像匹配方法。对不同条件下的同源和非同源的SAR图像进行了大量的实验,给出了初步的匹配结果。实践证明,对于本文研究的数据源,该算法稳定可靠,有很好的有效性和鲁棒性。     

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.     

机载红外图像转换生成卫星仿真图像的大气修正方法研究北大核心CSCD

基于机载红外热像仪测量的8~12μm波段图像,根据大气的吸收和热辐射影响红外成像仿真的理论,结合MODTRAN软件,采用“机—地—星法”和“机—星法”两种方法进行飞机平台到卫星平台间的大气修正。通过仿真数据和仿真图像两个方面的比较得出:两种大气修正方法得到的仿真图像效果较好;“机—星法”运算量小便于实时操作、“机—地—星法”更接近真实情况可用性强。     

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