SAR图像海洋表面油膜检测方法

海洋表面油膜对海洋环境影响极大,因此,及时获取海面油膜信息对保护海洋具有重要意义。目前各国采用的油膜检测方法主要有直接探测法和遥感方法。其中,遥感方法中的合成孔径雷达(SAR)是目前研究的热点。总结了SAR图像应用于海面油膜检测的主要特点,介绍并分析比较了SAR图像油膜检测的一般步骤及其实现方法。最后提出了SAR图像海洋表面油膜检测的发展方向。     

SAR图像海洋表面油膜检测方法

海洋表面油膜对海洋环境影响极大,因此,及时获取海面油膜信息对保护海洋具有重要意义。目前各国采用的油膜检测方法主要有直接探测法和遥感方法。其中,遥感方法中的合成孔径雷达(SAR)是目前研究的热点。总结了SAR图像应用于海面油膜检测的主要特点,介绍并分析比较了SAR图像油膜检测的一般步骤及其实现方法。最后提出了SAR图像海洋表面油膜检测的发展方向。     

利用SAR图像识别海面油膜的方法介绍

利用SAR图像可以有效地识别海上油膜。通过对南海及渤海地区的80多景ASAR数据进行暗区域分析,利用海面烃类油膜雷达遥感图像监测系统(System for Oil Spill Detection 1.0,简称SOSD 1.0)提取出油膜及低风速区等暗目标的特征参数,包括油膜的后向散射系数、均一度、梯度值、面积、复杂度等。通过对这些参数进行统计分析并结合人工解译,找出规律,形成根据这些规律以及其它的经验知识来区分油膜及非油膜的过程,并结合实例进行说明。     

基于ICA和SNF的SAR机场目标提取

针对合成孔径雷达(SAR)影像相干斑噪声强烈且分布形式及参数获取困难的问题,提出一种基于独立分量分析(ICA)和序列非线性滤波(SNF)实现多极化SAR影像相干斑噪声抑制和机场目标快速提取方法。利用ICA从多极化SAR影像中自动分离出图像数据与相干斑噪声,自动选择相干斑指数最小的分量为图像分量。通过SNF从分离出的图像分量中提取出机场目标。采用ENVISAT ASAR多极化影像进行实验,结果表明该方法能快速准确地提取多极化SAR影像中的机场目标。     

盲信号分离和序贯滤波的SAR影像水体提取

采用一种新的基于盲信号分离（BSS）和序列非线性滤波方法实现多极化合成孔径雷达（SAR）影像相干斑噪声抑制和水体目标快速提取。SAR影像具有强烈乘性相干斑噪声,影像数据为非高斯分布,但其具体分布形式及参数难以获得。利用基于独立分量分析的盲信号分离方法,不需要知道SAR影像的具体分布,通过对数量化将相干斑噪声转化为与图像数据相互独立的加性噪声,从多极化SAR影像中自动分离出图像数据与相干斑噪声,并自动选择相干斑指数最小的分量为图像分量。针对SAR影像水体目标的亮度及形状分布特征,进一步采用序列非线性滤波处理,从分离出的图像分量中提取出水体目标。利用ENVISAT ASAR多极化影像进行了实验,结果表明该方法可以快速准确地提取多极化SAR影像中的水体目标。     

多极化SAR图像斑点抑制综述

SAR(合成孔径雷达)图像斑点抑制一直是SAR图像应用中的一个重要课题。近二十几年来,伴随着SAR的发展和应用,涌现出了大量的斑点抑制算法。这些算法可分为两类:一类是基于单通道数据;另一类是基于多通道数据。对相干斑抑制问题进行了全面系统的研究,并侧重于多极化SAR图像的斑点抑制,归纳总结了近十几年来国内外所出现的多极化SAR图像斑点抑制算法,并对各种算法的性能进行了分析和比较。     

面向土地利用类型识别的高分辨率SAR数据复合技术研究

2007年以来,相继发射了3颗1m/3m高分辨率SAR卫星,极大地丰富了土地利用动态遥感监测数据源。SAR图像增强是土地利用动态遥感监测必要的预处理步骤。本文针对直接基于SAR数据进行土地利用类型识别中存在的问题,提出了SAR与光学图像、多时相SAR图像、多极化SAR图像合成和单极化SAR图像彩色合成等4种影像复合方法,分析评价了SAR与光学图像融合的应用效果,研究结果可为高分辨率极化SAR数据业务化应用提供技术参考。     

基于ENVISat的海洋污染性油膜雷达散射特征与扩散趋势分析

污染性海洋溢油一旦发生,快速获取油膜信息对有效控制溢油危害具有重要意义。以渤海湾一次溢油污染事件为例,利用ENVISat数据根据油膜雷达后向散射特征分析溢油的发生,并利用溢油期间的连续风场信息和连续SAR数据对比研究油膜的扩散趋势以及扩散过程中油膜尺度的变化。结果表明：污染性油膜在海上扩散的不同阶段具有不同的SAR图像特征,海上溢油雷达遥感检测分析方法与检测效果因SAR图像获取时油膜所处扩散阶段不同而有所不同,通过SAR连续观测结合辅助信息可以对污染油膜及其运动进行有效监控与预测。     

全极化SAR数据信息提取研究

全极化SAR(Synthetic Aperture Radar)测量的是每一像元的全散射矩阵,可合成包括线性极化、圆极化及椭圆极化在内的多种极化图像。因此与常规的单极化和多极化SAR相比,在雷达目标探测、识别、纹理特征的提取等方面全极化SAR具有很多优点。基于新疆和田地区的SIR-C L波段全极化雷达数据,介绍了极化合成的基本原理和数据处理流程,分析了几种典型地物全极化信号的特点,并在此基础上用监督分类法进行了全极化SAR数据的信息提取。结果表明:全极化SAR数据比单极化和多极化SAR数据具有更高的分类精度,并有效地的提取出地表信息,为利用SAR数据反演地表参数打下了基础。     

SAR图像船只分类识别研究进展

随着新一代SAR传感器的出现及应用,利用真实高分辨率、多极化SAR图像进行船只分类识别成为海上交通、渔业监测及国防应用的热点问题。首先回顾了SAR图像船只分类识别技术的发展。以近20 a国内外研究的重要成果为基础,对几何结构特征、散射特征和极化特征等船只分类特征进行了总结和比较,分析了其优缺点;总结了各种SAR图像船只分类识别算法的技术特点,并分析了各方法的适用性。最后对SAR图像船只分类识别技术的应用前景和发展趋势进行了阐述。     

SAR图像在韩国溢油监测中的应用

利用SAR图像,对2007年12月发生在韩国的溢油事件进行监测。首先分析了SAR图像监测溢油的原理和限制,然后以ENVISAT\|ASAR数据为例,分析SAR数据处理与信息提取过程,其中包括:进行几何精校正、对图像进行增强处理、滤波、分类。最后根据SAR图像综合信息,解译勾画出溢油信息边界,利用GIS系统叠加风场、地形、其它重要要素数据,对溢油的分布、扩散以及对周边环境的影响进行分析评价,为决策者提供决策支持。     

海底油渗SAR探测综述

海底油渗来源于海底石油的自然渗漏，及时地获得海底油渗信息对探测、开发海底石油资源具有重大意义。目前，合成孔径雷达（Synthetic Aperture Radar, SAR）已经成为海底油渗探测的新型辅助手段。首先总结了海底油渗SAR探测机理，包括海底油渗形成机制、海底油渗SAR成像原理和海底油渗SAR图像特征;其次介绍了海底油渗SAR探测方法;然后给出了目前国际上海底油渗SAR探测的应用现状;最后提出了海底油渗SAR探测的现存问题和未来发展方向。     

On the co‐polarized phase difference for oil spill observation

In this study dual‐polarized synthetic aperture radar (SAR) measurements were used to enhance oil spill observation. The co‐polarized phase difference (CPD) was modelled and used to characterize the scattering return from oil spills and biogenic slicks. The model predicts, under low to moderate wind conditions, a larger CPD standard deviation (σ) for oil with respect to the sea, while for biogenic slicks a σ value similar to that for the sea is obtained. Experiments accomplished with multilook complex (MLC) C‐ and L‐band SAR data show that the model predictions are confirmed and that the C‐band is, as expected, to be preferred to the L‐band.     

Oil spill detection using marine SAR images

A probabilistic approach to distinguish oil spills from other similar oceanic features in marine Synthetic Aperture Radar (SAR) images has been developed and tested. The method uses statistical information obtained from previous mesurements of physical and geometrical characteristics for both oil spill and natural features. A sample image is evaluated using two different procedures to determine the probability that it is an oil spill, the results of the two procedures are then compared. The classification-algorithm performance was evaluated using a test dataset containing 80 examples that were oil spills and 43 that were natural features exhibiting characteristics similar to oil spills: more than 80% of the samples were classified correctly. The reliability of the method was then determined using a new dataset and similar results were obtained.     

Oil spill detection from SAR intensity imagery using a marked point process

This paper presents a new algorithm for the detection of oil spill from SAR intensity images. The proposed algorithm combines the marked point process, Bayesian inference and Markov Chain Monte Carlo (MCMC) technique. In this paper, the candidates of oil spills or dark spots in a SAR intensity image are characterized by a Poisson marked point process. The marked point process is formed by a group of random points (as a point process modelling the locations of oil spills) and a set of parameters including geometric parameters of windows centred at the random points and gamma distribution parameters (as the marks attaching to each point). As a result, the candidates of oil spills are represented by a group of windows, in which the intensities of pixels follow independent and identical gamma distribution with lower mean than that for the identical gamma distribution of the pixels out of windows. Following the Bayesian paradigm, the posterior distribution, which characterizes the locations and statistical distributions of oil spills, can be obtained up to a normalizing constant. In order to simulate from the posterior distribution and to estimate the parameters of the posterior distribution, the Revisable Jump MCMC (RJMCMC) algorithm is used. The optimal locations and sizes of dark spots are obtained by a maximum a posteriori (MAP) algorithm. The proposed approach is tested using Radarsat-1 SAR images with oil spills indicated by human analysts. The results show that the proposed approach works well and is very promising.     

SAR影像海洋表面溢油检测方法与实现

海洋是地球的重要组成部分,它为人类提供了丰富的物质和宝贵的资源,每年海洋都承受着不同程度的侵害,其中油类污染是给海洋造成巨大危害的污染之一。而油类污染又主要来源于轮船破裂漏油以及油井平台或海底输油管道爆炸等。每次事故造成的直接经济损失达几百万至上千万不等,所以对海上溢油进行监测具有重要的意义。选用Envisat的ASAR数据进行海上溢油检测,介绍并分析了SAR图像溢油检测的一般步骤及其实现方法,通过采用单一阈值分割法、最大熵分割法和非监督分类法对影像进行目标检测,从而粗略地将影像区分为前景区域与背景区域,并结合影像的纹理特征进行分类。在纹理特征选取过程中,通过人工选取部分溢油区与非溢油区作为感兴趣区,在感兴趣区上分别统计SAR影像常用的纹理特征,并结合不同目标检测的结果以及原始影像进行基于BP神经网络的分类,得到了良好的效果。最后展望了SAR图像海洋溢油检测的发展方向。     

On the interpretation of SAR imagery from the Sea Empress oil spill

A method for monitoring oil spills using SAR imagery is suggested, based on the simulation of the wave spectrum using modelled surface winds. A first order separation of the purely wind-driven backscatter distribution and its modification due to surfactant was made by parametrizing the effect of surfactant on the wave growth rate and on the reflective properties of the sea surface. The technique was applied to a SAR image showing the Sea Empress oil spill, in south-west Wales, UK.     

Automatic detection of oil spills from SAR images

A probabilistic method has been developed that distinguishes oil spills from other similar sea surface features in synthetic aperture radar (SAR) images. It considers both the radiometric and the geometric characteristics of the areas being tested. In order to minimize the operator intervention, it adopts automatic selection criteria to extract the potentially polluted areas from the images. The method has an a priori percentage of correct classification higher than 90% on the training dataset; the performance is confirmed on a different dataset of verified slicks. Some analyses have been conducted using images with different radiometric and geometric resolutions to test its suitability with SAR images different from European Remote Sensing (ERS) satellite ones. The system and its ability to detect and classify oil and non‐oil surface features are described. Starting from a set of verified oil spills detected offshore and over the coastline, the ability of SAR to reveal oil spills is tested by analysing wind intensity, deduced from the image itself, and the distance from the coast.