水平集方法在卫星遥感图像海冰边缘检测中的应用

针对SAR等高分辨率卫星数据在海冰业务化监测工作中的应用前景,该文将水平集方法和多尺度小波方法相结合,对海冰外缘线提取方法进行研究;其提取结果可以用于海冰面积估算、密集度估算、冰间水道提取和海冰类型区分等。渤海、南极海域的SAR数据和渤海海域的HJ-1A卫星数据实例分析说明了所提出方法的有效性。     

一种联合多特征的极化SAR海冰类型提取方法

针对仅依靠单一属性特征难以实现海冰类型精准监测的问题,提出一种联合极化目标分解特征和纹理特征的全极化SAR海冰类型提取方法:首先利用H/α/A分解和AnYang分解获取海面目标的6个极化分解特征;然后通过灰度共生矩阵获取HV极化图像的3个纹理特征,进而将极化分解特征和纹理特征组合构建9个特征的联合特征矢量;最后基于支持向量机分类器,实现极化SAR图像海冰类型的精确提取。以渤海辽东湾为实验区,选用高分三号全极化SAR数据,利用本文构建的海冰类型提取方法,获取了实验区的海冰类型的分布信息,并与其他提取方法进行了对比分析。实验表明,本文构建的9个联合特征矢量,特征之间具有较好的互补性,提高了不同海冰类型之间的区分度,改善了海冰类型提取的精度,总体分类精度为92.6%,Kappa系数为0.87。     

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。     

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。     

采用相干斑抑制图割的SAR海冰图像分割

针对SAR海冰图像分割受相干斑噪声干扰严重的问题,在MRF框架下,提出一种分割新算法—SRGB-RMRF。算法首先根据相干斑噪声统计特性,对传统graph-based方法的梯度和区域内部差异计算公式重新定义,得到适用于SAR图像的相干斑抑制graph-based(SRGB)初始分割新方法。其次,结合区域间强度差异,在SRGB方法得到的区域邻接图上构建区域MRF模型。在合成SAR海冰图像和真实SAR海冰图像上的实验结果表明,与现有区域MRF算法相比,SRGB-RMRF算法能够实现更为准确的SAR海冰图像分割。     

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。     

NOAA卫星实时监测海冰的业务方法

渤海和黄海北部海域的海冰是海洋环境实时监测和业务预报的重要项目之一。本文介绍NOAA卫星定量数据用于实时监测渤海海冰的业务化方法,其中包括海冰业务产品的形式及其数据和图像处理的方法。一、前言一般年份的冬季,渤海海面都有结冰,其中以辽东湾为最甚,但总的冰情并不严重,不     

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。     

基于SAR影像海冰动态特征的提取方法

海冰边缘区(Marginal Ice Zone,MIZ)海冰种类繁多,具有较强的动态性,充分了解其覆盖范围及变化对于研究海冰变化、环境变化以及更好地开展人类活动等方面具有重要意义。以Radarsat-2SAR影像为例,根据MIZ在影像中的不同尺度和方向上的曲线特征,采用能够将图像分解为多尺度多方向信息的曲波变换进行特征提取。利用中尺度曲波系数邻域内的均值和灰度共生矩阵(GLCM)的能量值之间的相互关系,设计并实现了一种基于SAR影像的海冰动态特征的提取方法。利用此动态特征得到的MIZ与采用海冰密集度数据定义的MIZ相比较,准确率得到大幅的提升。结果表明:该特征能够有效地描述海冰的动态程度,为MIZ的识别算法提供基础和新的研究思路,同时能够为海冰分析模型、环境预测模型等提供有效参数。     

基于数据集市的渤海海冰数值预报系统的设计与实现

以Visual Studio 2008和Visual Fortran 8.0作为平台开发基于数据集市的渤海海冰数值预报系统。系统将海冰数值预报所需的初始场、数值预报模式、数值预报结果展示等集成到同一平台下,以庞大的北海区海洋观测预报综合数据集市为基础,形成了"初始场输入-预报参数输入-数值模拟计算-成果展示"的完整链条。     

Discrimination of sea ice in the Labrador marginal ice zone from synthetic aperture radar image texture

This study addresses the modelling of synthetic aperture radar (SAR) image texture for sea ice scenes in the Labrador marginal ice zone (MIZ). The image texture of distributed scatterers contains a substantial component relating to the imaging system as well as information about the scene itself. Theory shows that the image autocorrelation function (ACF) may be analysed to separate system contributions from scene contributions under certain conditions. The main theses of the study are: (i) SAR intensity images of sea ice are spatially nonGaussian; and (ii) the predominant types and forms of MIZ sea ice may be discriminated based upon ACF model parameters. Experimental results indicate that the model provides an excellent fit to the measured ACFs. The image texture was found to be a strong function of the form of the sea ice as well as its type. For a given type, the various forms could be discriminated through a single SAR channel. For full discrimination of all types and forms observed, a two-channel combination was necessary: XHV CHH or XHV CHV.     

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.     

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。     

Unsupervised segmentation of ERS and Radarsat sea ice images using multiresolution peak detection and aggregated population equalization

In this paper we describe Automated Sea Ice Segmentation (ASIS), a system that automatically segments Synthetic Aperture Radar (SAR) sea ice imagery. This system integrates image processing, data mining, and machine learning methodologies to determine the number of visually separable classes in ERS and Radarsat sea ice images. We introduce two new techniques: multiresolution peak detection and spatial clustering. The detection is a noise-resistant data discretization methodology that results in an initial segmentation of the image. The clustering is based on an innovative concept called Aggregated Population Equalization that utilizes spatial relationships among classes to merge and split the population environment. Its self-organizing ability produces the final segmentation and automates ASIS. In addition, we have designed a Java-based graphical user interface that facilitates post-segmentation human evaluation and classification. Thus, ASIS can be used as a pre-processor to help analyse sea ice images as well as a basis for human classification of sea ice images. We have tested the system on more than 300 ERS-1, ERS-2 and Radarsat SAR sea ice images and analysed the results to point out the strengths and weaknesses of ASIS in the automated segmentation of sea ice images.     

遥感技术监测海冰密集度

概要评述可见近红外、主/ 被动微波遥感技术监测海冰密集度的基本原理、算法及其优缺点。着重介绍和讨论被动微波传感器SMMR 和SMM/ I 遥感图像混合像元内海冰总密集度, 一年海冰及多年海冰密集度的NASA 算法及其天气滤波器。     

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.     

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.