Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks

A method for segmentation and classification of Baltic Sea ice synthetic aperture radar (SAR) images, based on pulse-coupled neural networks (PCNNs), is presented. Also, automated training, which is based on decomposing the total pixel value distribution into a mixture of class distributions, is presented and discussed. The algorithms have been trained and tested using logarithmic scale Radarsat-1 ScanSAR Wide mode images over the Baltic Sea ice. Before the decomposition into mixture of class distributions, an incidence angle correction, specifically designed for these Baltic Sea ice SAR images, is applied. Because the data distributions in the uniform areas of these images are very close to Gaussian distributions, the data are decomposed into a mixture of Gaussian distributions, using the Expectation-Maximazation algorithm. Only uniform image areas are used in the decomposition phase. The mixture of distributions is compared to the distributions of the Baltic Sea ice classes, based on earlier scatterometer measurements and visual video interpretations of the sea ice classes. The parameter values for the PCNN segmentation are defined based on this mixture of distributions. The PCNN segmentation results are also compared to the operational sea ice information of digitized ice charts and to visual interpretation of the sea ice class.     

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.     

融合极化旋转域特征和超像素技术的极化SAR舰船检测

对海监视是极化SAR的重要应用,密集区域的舰船目标检测是当前面临的主要挑战之一。舰船密集区域受多目标串扰,传统的恒虚警率(CFAR)检测滑窗难以选取纯净的海杂波样本用于确定检测门限,将导致检测性能下降。针对这一问题,该文从特征提取和检测器设计两方面出发,提出一种融合极化旋转域特征和超像素技术的极化SAR舰船检测方法。在特征提取方面,雷达目标的后向散射敏感于目标姿态与雷达视线的相对几何关系,由此带来的散射多样性隐含信息可通过极化旋转域分析进行挖掘。该文利用极化相关方向图及导出的一系列极化旋转域特征,根据目标杂波比(TCR)分析,优选TCR最高的3个极化特征量用于构建目标检测器。在此基础上,该文在检测器设计方面提出了一种基于K均值聚类的杂波超像素筛选方法,有效避免了密集区域舰船目标对邻近杂波的影响,基于筛选的杂波像素点得到舰船目标CFAR检测结果。基于Radarsat-2和高分三号星载全极化SAR数据的对比实验表明,所提方法能有效实现密集区域舰船目标检测,检测品质因数达到95%。      

Surface roughness characterizations of sea ice and ice sheets: case studies with MISR data

This work is an examination of potential uses of multiangular remote sensing imagery for mapping and characterizing sea ice and ice sheet surfaces based on surface roughness properties. We use data from the Multi-angle Imaging SpectroRadiometer (MISR) to demonstrate that ice sheet and sea ice surfaces have characteristic angular signatures and that these angular signatures may be used in much the same way as spectral signatures are used in multispectral classification. Three case studies are examined: sea ice in the Beaufort Sea off the north coast of Alaska, the Jakobshavn Glacier on the western edge of the Greenland ice sheet, and a region in Antarctica south of McMurdo station containing glaciers and blue-ice areas. The MISR sea ice image appears to delineate different first-year ice types and, to some extent, the transition from first-year to multiyear ice. The MISR image shows good agreement with sea ice types that are evident in concurrent synthetic aperture radar (SAR) imagery and ice analysis charts from the National Ice Center. Over the Jakobshavn Glacier, surface roughness data from airborne laser altimeter transects correlate well with MISR-derived estimates of surface roughness. In Antarctica, ablation-related blue-ice areas, which are difficult to distinguish from bare ice exposed by crevasses, are easily detected using multiangular data.     

A wavelet-based algorithm to estimate ocean wave group parameters from radar images

In recent years, new remote sensing techniques have been developed to measure two-dimensional (2-D) sea surface elevation fields. The availability of these data has led to the necessity to extend the classical analysis methods for one-dimensional (1-D) buoy time series to two dimensions. This paper is concerned with the derivation of group parameters from 2-D sea surface elevation fields using a wavelet-based technique. Wave grouping is known to be an important factor in ship and offshore safety, as it plays a role in dangerous resonance phenomenons and the generation of extreme waves. Synthetic aperture radar (SAR) data are used for the analysis. The wavelet technique is introduced using synthetic ocean surfaces and simulated SAR data. It is shown that the group structure of the ocean wave field can be recovered from the SAR image if the nonlinear imaging effects are moderate. The method is applied to a global dataset of European Remote Sensing satellite (ERS-2) wave mode data. Different group parameters including the area covered by the largest group and the number of groups in a given area are calculated for over 33 000 SAR images. Global maps of the parameters are presented. For comparison, classical 1-D grouping parameters are calculated from colocated wave model data showing good overall agreement with the wavelet-derived parameters. ERS-2 image mode data are used to study wave fields in coastal areas. Waves approaching the island of Sylt in the North Sea are investigated, showing the potential of the wavelet technique to analyze the spatial wave dynamics associated with the bottom topography. Observations concerning changes of wavelength and group parameters are compared to linear wave theory.     

环境一号C 卫星SAR 图像典型环境遥感应用初探

环境一号C 卫星(HJ-1C)于2012 年11 月19 日成功发射,并与2008 年9 月6 日一箭双星发射的环境一号A 星,B 星(HJ-1A/1B)组成2+1环境与灾害监测预报小卫星星座系统。该文以2012 年12 月～2013 年1 月期间获取的9 景HJ-1C 卫星数据2 级产品(S 波段,VV 极化,Strip 模式,5 m 分辨率)为实验数据,以北京市和福建省近海海域为研究实验区,以HJ-1C 卫星SAR 图像土地利用类型人工解译与制图、地表覆盖自动分类、近海海洋溢油污染识别、海浪特征参数反演、海水养殖区特征提取等近海海洋环境监测等为例,开展了HJ-1C卫星SAR 图像环境遥感应用能力的分析与评价。结果表明:(1)HJ-1C 卫星SAR 图像可用于耕地、林地、公路用地、河流水面、城镇住宅用地、农村宅基地等土地利用类型的人工解译和制图,地类图斑面积勾绘误差小于5%;(2)HJ-1C 卫星SAR 与HJ-1B CCD 图像融合可有效提高地表覆盖自动分类精度;(3)HJ-1C 卫星SAR 图像可用于海洋溢油污染识别、海浪特征参数反演及近海养殖区信息提取等近海海洋环境遥感监测。      

SAR Polarimetry to Observe Oil Spills

A study on sea oil spill observation by means of polarimetric synthetic aperture radar (SAR) data is accomplished. It is based on the use of a polarimetric constant false alarm rate filter to detect dark patches over SAR images. Then, the target decomposition theorem is exploited to distinguish oil spills and look-alikes. Experiments are conducted on polarimetric SAR data acquired during the SIR-C/X-SAR mission on October 1994. The data were processed and calibrated at the Jet Propulsion Laboratory, National Aeronautics and Space Administration. Results show that the new polarimetric approach is able to assist classification     

On shape, orientation, and structure of atmospheric cells inside wind rolls in two SAR images

Synthetic aperture radar (SAR) images of the sea surface often show roll-vortex structures and other features which, in general, are spread out over several length scales and may present spatial periodicity as well as intermittence. Standard techniques, such as two-dimensional (2-D) Fourier analysis, are unsuitable both when it is of interest to detect intermittent phenomena and to analyze the spatial disposition of the backscatter structures inside the SAR images. For the above reasons, the 2-D continuous wavelet transform analysis has been applied to two European Remote Sensing mission SAR images over the Mediterranean Sea, showing wind rolls and atmospheric gravity waves. Through the evaluation of the wavelet variance map, which ideally corresponds to the 2-D Fourier spectrum, it has been possible to assess the presence of two main energy areas at large (from 7-28 km) and small (from 0.5-2 km) spatial scales. While the large-scale fluctuations may be ascribed to atmospheric gravity waves and other features induced by the surroundings, the small-scale fluctuations reveal the inner structure of the atmospheric wind rolls. The SAR-like maps, obtained by adding the wavelet coefficient maps pertaining to the small scales, have permitted us to highlight the high- and low-intensity backscatter cells associated with the wind rolls. These cells have been statistically characterized by means of the frequency distributions of the size of the cells maximum and minimum axes, of the orientation of the maximum axis, and of their area. The results indicate that high- and low-intensity backscatter cells have similar characteristics in both cases studied: they appear of elliptic shape, with the major axis along the wind roll direction; the average axes ratio is 2.5:1. The frequency distributions of the cell area indicate a continuous distribution of sizes, without significant gaps.     

SAR Remote Sensing Analysis of the Sea Surface by Polynomial Filtering [Applications Corner]

Synthetic aperture radar (SAR) allows the observation of the sea surface over large areas regardless of weather conditions. In what follows we discuss a digital signal processing (DSP) formalism that makes use of polynomial filters such as Volterra models to extract the geophysical information from SAR images and to model several nonlinear transfer functions. Polynomial filters allow the extension of algorithms derived for the linear case to the nonlinear case. First, we will briefly discuss the types and sources of nonlinearities in SAR mapping of the ocean surface. Next, we will summarize the main characteristics of the Volterra filters and apply them to the understanding of hydrodynamic nonlinearities and instrumental nonlinearities. Then, we will combine their Volterra models to model the complete mapping process. Although we have only focused on the particular example of Volterra filters here, nonlinear autoregressive moving average (NARMA) models can also been applied to extract geophysical information from a nonlinear marine feature signature.     

Analyzing lead information from SAR images

Leads are relatively linear features in the sea ice cover, which are composed of open water or new, thin ice. Because of their composition, leads impact the ocean/air heat exchange. Automated analysis of leads from sea ice imagery may provide a means of gathering important information about the sea ice cover and its climatic influence. This paper describes: (1) a method for extracting and analyzing leads from ERS-1 synthetic aperture radar (SAR) images classified by ice type and (2) the results of using this method on images of the Beaufort Sea. The methodology consists of identifying potential lead features in the image and measuring their characteristics both before and after using a thinning or skeletonization technique on the features. The measurements obtained using this method include lead area, average width, number of leads in an area, amount of branching, and linearity of the lead. These measurements were analyzed with respect to the time of year and the latitude of the images. Results indicate that the measurements produced by the methodology are consistent with lead measurement distributions that others have found. The results of the study suggest that the methodology is appropriate to study lead characteristics on a large scale     

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.     

Sea surface imaging with an across-track interferometric syntheticaperture radar: the SINEWAVE experiment

An across track interferometric synthetic aperture radar (InSAR) is used to image ocean waves. Across track InSAR data were acquired during the SAR INnterferometry Experiment for validation of ocean Wave imaging models (SINEWAVE) in the North Sea using an airborne X-band radar with horizontal polarization. A wind sea system was imaged at different flight levels and with different flight directions with respect to the ocean wave propagation direction. Simultaneously, ocean wave spectra were measured by a directional wave rider buoy. Thus, the experiment data comprises synthetic aperture radar (SAR) intensity, coherence, and phase images together with in situ measurements. As shown in a recent theoretical study by Schulz-Stellenfleth and Lehner (2001), across track InSAR provides distorted (bunched) digital elevation models (DEMs) of the sea surface. Using SINEWAVE data the DEM bunching mechanism is verified with in situ ocean wave measurements available for the first time. It is shown that significant waveheight as well as one-dimensional (1D) wavenumber spectra derived from bunched DEMs and buoy data are in good agreement for small nonlinearities. Peak wave directions and peak wavelength detected in bunched DEMs and SAR intensity images are compared with the buoy spectrum. Peak rotations of up to 30° with respect to the buoy spectrum are found depending on flight direction and flight level. Two-dimensional (2D) spectra of bunched DEMs, corresponding coherency maps, and SAR intensity images are intercompared. The signal-to-noise ratio (SNR) of bunched DEM spectra is shown to be about 5 to 10 dB higher than the SNR of SAR intensity image spectra     

弹载合成孔径雷达动态海面成像模拟研究

海面成像模拟是对海作战导弹弹载合成孔径雷达(SAR)成像制导全过程动态仿真的重要组成部分,对于系统的性能分析和优化设计具有重要意义。研究了弹载情况下SAR对海成像模拟问题,提出了基于三维海面波动模型及弹载SAR工作过程的成像模拟方案。出于逼真性及运算速度的考虑,仿真时动态海面选用基于海浪谱的双尺度模型,海面散射模型选用双尺度散射模型。结合一组典型弹载SAR系统参数,分别在正侧视和斜视模式下对三种典型海况的动态海面进行了成像模拟,并利用实际海面SAR图像的统计特性验证了模拟结果的有效性。     

基于级联网络的复杂大场景SAR图像舰船目标快速检测

合成孔径雷达(synthetic aperture radar,SAR)图像舰船目标检测紧贴军事和民用需求,为海洋监视提供重要信息支撑.针对复杂大场景SAR图像,本文设计了一种基于级联网络的舰船目标检测框架,该网络框架主要由D-BiSeNet海陆分割、分块区域筛选和CP-FCOS目标检测三部分组成.通过改进双边网络(D...     

AIR-SARShip-1.0:高分辨率SAR舰船检测数据集

近年来,深度学习技术得到广泛应用,然而在合成孔径雷达(SAR)舰船目标检测研究中,由于数据获取难、样本规模小,尚难以支撑深度网络模型的训练。该文公开了一个面向高分辨率、大尺寸场景的SAR舰船检测数据集,该数据集包含31景高分三号SAR图像,场景类型包含港口、岛礁、不同级别海况的海面等,背景涵盖近岸和远海等多样场景。同时,该文使用经典舰船检测算法和深度学习算法进行了实验,其中基于密集连接端到端网络方法效果最佳,平均精度达到88.1%。通过实验对比分析形成指标基准,方便其他学者在此数据集基础上进一步展开SAR舰船检测相关研究。      

0.4~14 μm中国海域海表反照率时空分布特性

复杂海洋环境是空间光电探测的典型应用场景,可见至长波红外波段海表反照率参数是海洋大气传输与背景辐射特性研究的重要内容。鉴于目前中国海域海表反照率数据缺乏的现状,在多源卫星长时间观测海洋环境特性参数数据的统计分析基础上,通过考虑海水叶绿素含量、气溶胶光学厚度、海表面风速和太阳入射角度等参数的复合影响,建立了海表反照率的快速参数化应用模式,获得了中国南海、东海、黄海和渤海等典型海域0.4~14m海表反照率谱线,掌握了海表反照率的时空分布特征,为海空光电工程应用提供基础数据支撑。     

An autoregressive moving average tone detector

A new method for estimating tones in an arbitrary spectrum is presented. An autoregressive-moving average estimator is formulated and transformed into a linear regression problem. Many of the shortcomings of an "all pole" model are overcome and simulated test results indicate that the estimates are not particularly sensitive to additive noise. The main advantages of this new method are computational simplicity and robustness in noise environments. The algorithm can be useful in all areas where spectral information must be extracted in a computationally efficient fashion.