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.     

An object‐oriented methodology to detect oil spills

A new automated methodology for oil spill detection is presented, by which full synthetic aperture radar (SAR) high‐resolution image scenes can be processed. The methodology relies on the object‐oriented approach and profits from image segmentation techniques to detected dark formations. The detection of dark formations is based on a threshold definition that is fully adaptive to local contrast and brightness of large image segments. For the detection process, two empirical formulas are developed that also permit the classification of oil spills according to their brightness. A fuzzy classification method is used to classify dark formations as oil spills or look‐alikes. Dark formations are not isolated and features of both dark areas and sea environment are considered. Various sea environments that affect oil spill shape and boundaries are grouped in two knowledge bases, used for the classification of dark formations. The accuracy of the method for the 12 SAR images used is 99.5% for the class of oil spills, and 98.8% for that of look‐alikes. Fresh oil spills, fresh spills affected by natural phenomena, oil spills without clear stripping, small linear oil spills, oil spills with broken parts and amorphous oil spills can be successfully detected.     

基于CFAR海上溢油检测研究

在SAR图像处理的基础上,提出一种新的基于恒虚警率( CFAR-Constant False Alarm Rate) 技术,确定SAR 图像中检测溢油整体阈值的方法。该方法采用高斯分布(正态分布) 作为SAR 图像灰度的概率密度函数,由CFAR 技术直接导出用于检测海上溢油整体阈值的计算公式,进行虚警去除。该算法避免了复杂公式迭代和求解形状参数计算过程,也避免了用二分法寻找阈值的循环解算过程,提高了检测速度。使用ENVISAT图像对该算法进行检验,结果显示所提出的算法在检测精度和检测速度上都有明显的改进。     

基于极化雷达影像的海岛礁提取

提出了一种利用RADARSAT-2全极化SAR影像和极化特征参数提取精确的海岛礁范围的技术方法。极化特征熵参数描述了目标散射的随机性,与海水相比海岛礁处于较高的去极化状态,因此海岛礁的熵值明显大于海水的熵值。首先本文利用EM(Expectation Maximization,最大数学期望)算法自动计算的提取海岛礁最佳阈值对熵参数文件进行阈值分割,得到海岛礁的初始分割结果。由于受到船只和海水表面波浪的影响,海水部分也会存在与海岛礁近似的熵值。因此初步阈值分割得到的海岛礁结果会有部分海水和船只等,利用PSNR(Peak value signal-to-noise ratio,峰值信噪比)提取海水大致范围并剔除海水范围内初始分割结果中的噪声部分。最后根据TM影像提取的海岛礁范围进行精度评价,实验结果表明该技术方法能够从极化SAR影像上准确提取海岛礁范围。     

Intelligent hybrid system for dark spot detection using SAR data

Synthetic Aperture Radars (SAR) are the main instrument used to support oil detection systems. In the microwave spectrum, oil slicks are identified as dark spots, regions with low backscatter at sea surface. Automatic and semi-automatic systems were developed to minimize processing time, the occurrence of false alarms and the subjectivity of human interpretation. This study presents an intelligent hybrid system, which integrates automatic and semi-automatic procedures to detect dark spots, in six steps: (I) SAR pre-processing; (II) Image segmentation; (III) Feature extraction and selection; (IV) Automatic clustering analysis; (V) Decision rules and, if needed; (VI) Semi-automatic processing. The results proved that the feature selection is essential to improve the detection capability, keeping only five pattern features to automate the clustering procedure. The semi-automatic method gave back more accurate geometries. The automatic approach erred more including regions, increasing the dark spots area, while the semi-automatic method erred more excluding regions. For well-defined and contrasted dark spots, the performance of the automatic and the semi-automatic methods is equivalent. However, the fully automatic method did not provide acceptable geometries in all cases. For these cases, the intelligent hybrid system was validated, integrating the semi-automatic approach, using compact and simple decision rules to request human intervention when needed. This approach allows for the combining of benefits from each approach, ensuring the quality of the classification when fully automatic procedures are not satisfactory.     

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。     

Typhoon eye extraction with an automatic SAR image segmentation method

Spaceborne microwave synthetic aperture radar (SAR), with its high spatial resolution (10–100 m), large area coverage, and day/night imaging capability, has been used as an important tool for typhoon monitoring. Since the microwave signal can penetrate through clouds, SAR images reveal typhoon morphology at the sea surface. Within the region of a typhoon eye, wind speed and the associated sea surface roughness are usually low. Therefore, the typhoon eye can be well distinguished as dark areas in SAR images. However, automatic typhoon eye extraction from SAR images is hampered by SAR image speckle noise and other false-alarm dark features contained in an image. In this study, we propose an image processing approach to extract typhoon eyes from SAR images. The three-step image processing includes: (1) applying an extended non-local means image denoizing algorithm to reduce image speckle noise; (2) applying a top-hat transform to denoized imagery to enhance the contrast; and (3) using a labelled watershed to segment the typhoon eye. Experimental results from analysing three Environmental Satellite SAR typhoon images show that our approach provides fast and efficient SAR image segmentation for typhoon eye extraction. Typhoon eyes are segmented correctly, and their edges are well detected. Our experimental results are comparable to manually extracted typhoon eye information. Fine-tuning of this approach will provide an automatic tool for typhoon eye information extraction from SAR images.     

海洋监视监测SAR卫星任务初步分析

以海浪、船舶、溢油、内波和海冰等海洋现象为主要的观测目标,海洋监视监测卫星利用多极化SAR的高分辨率模式进行精细成像,识别目标细节特征,利用宽观测带扫描模式进行快速覆盖观测,获取全球海洋遥感信息。针对各观测目标要素,借鉴ERS和RADARSAT-1的观测结果,结合理论分析与仿真,初步分析了海洋监视监测SAR卫星轨道参数和有效载荷工作模式等卫星任务。     

SAR与SPOT数据融合方法研究

随着多空间分辨率、多光谱分辨率、多传感器遥感数据日益增多,数据融合技术已经在地学领域得到广泛应用。以SAR和SPOT多光谱影像为数据源,通过几何校正、辐射定标、噪声去除等预处理,采用PCA、IHS、BT、HPF以及WT融合方法进行融合试验,选取标准差、信息熵以及相关系数对融合结果进行图像质量评价。结果表明：基于结构信息变换的HPF和WT方法,纹理结构信息保持较好,线性地物特征尤为突出;基于统计信息变换的PCA方法,不仅能较好保持多光谱信息,而且也保持了SAR影像纹理结构信息,信息熵指数最大;基于彩色变换的IHS和BT方法,虽然视觉效果较理想,但是色彩及光谱失真现象严重,信息熵值最小。      

NCEP/QSCAT混合风向用于SAR图像反演高分辨率海面风速

SAR图像反演高分辨率海面风速在微波遥感领域具有重要的意义。利用 SAR图像和NCEP/QSCAT混合风向数据,对NCEP/QSCAT混合风向用于SAR图像反演高分辨率海面风速的方法进行了初步研究。以2005年12月6日一景ENVISat ASAR图像为例,反演了大范围、高分辨率海面风速。海面风速的反演结果与NCEP/QSCAT混合风速、日平均散射计风速的比较结果显示,其均方根误差分别为1.9 m/s、1.6 m/s,二者符合较好,显示了SAR反演高分辨率海面风速的能力与SAR海面风场业务化应用的前景。     

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。     

High-resolution satellite measurements of coastal wind field and sea state

Methods to derive wind speed and sea state by simple empirical models from synthetic aperture radar (SAR) data are presented and applied for use in high-resolution numerical modelling for coastal applications. The new radar satellite, TerraSAR-X (TS-X), images the surface of the sea with a high resolution up to 1 m. Therefore, not only wind information and integrated sea state parameters but also individual ocean waves with wavelengths down to 30 m are detectable. Two-dimensional information on the ocean surface retrieved using TS-X data is validated for different oceanographic applications: derivation of finely resolved wind fields (XMOD algorithm) and integrated sea state parameters (XWAVE algorithm). Both algorithms are capable of taking into account fine-scale effects in coastal areas. Wind and sea state information retrieved from SAR data are applied as the input for a wave numerical spectral model (wind forcing and boundary condition) running at a fine spatial horizontal resolution of 100 m. Results are compared to collocated buoy measurements. Studies are carried out for varying wind speeds and comparisons against wave height, simulated using original TS-X-derived wind data, showing the sensitivity of waves to local wind variation and thus the importance of local wind effects on wave behaviour in coastal areas. Examples for the German Bight (North Sea) are shown. The TS-X satellite scenes render well-developed ocean wave patterns of developed swell at the sea surface. Refraction of individual long swell waves at a water depth shallower than about 70 m, caused by the influence of underwater topography in coastal areas, is imaged on the radar scenes. A technique is developed for tracking wave rays depending on changes in swell wavelength and direction. We estimate the wave energy flux along wave tracks from deep water to the coastline based on SAR information: wave height and wavelength are derived from TS-X data.     

SAR探测水下运动物体波浪尾迹的模拟研究

水下运动物体可以在海面产生波浪,这种波浪与海洋表面波相互作用改变了海浪谱的高低频分布而形成波浪尾迹。将这样的波-波相互作用处理为对背景海浪谱的扰动,利用海面微波散射的二尺度模型,分析波浪尾迹对雷达散射系数的影响,并给出相应的数值计算模型;对水下不同运动状态物体的波浪用汇源分布法计算出波面函数,将它们分别与一些特定海况的海浪波面函数叠加,并对之进行微波后向散射测量仿真计算,给出波浪尾迹的SAR(Synthetic Aperture Radar、合成孔径雷达)探测图像;再利用二维谱分析技术对模拟SAR图像进行处理,提取波浪尾迹信息。初步结果表明,SAR探测下运动物体是可行的。
     

SAR detection and characterization of sea surface slicks

In this paper we demonstrate SAR system capability for detecting and characterizing marine surface slicks. During an aircraft measurement campaign over the Gulf of Genoa (Italy), a multi-frequency SAR system, operating in P-, L- and C-bands, explored a sea area heavily covered by slicks. At the same time in situ measurements were performed with an interferential microwave probe, installed on board a small boat, capable of measuring high resolution sea spectra up to frequencies of capillary waves. By plotting SAR pixel intensity versus sea wave Bragg frequency we obtained wide portions of the sea spectrum region affected by the surface film damping. Spectra derived from SAR imagery and from gauge data present comparable slopes and furthermore the ratio between clean to slicked water spectrum obtained with the two techniques were surprisingly similar. This demonstrates the multi-frequency SAR systems ability to detect and characterize sea surface films assuming the Bragg mechanism in the radar backscatter. The outlined analysis suggests a simple methodology to monitor coastal water quality by using airborne SAR.     

Robust Satellite Techniques for oil spill detection and monitoring using AVHRR thermal infrared bands

In this article, a new satellite technique for oil spill detection and monitoring is fully presented and discussed. It is based on the general RST (Robust Satellite Techniques) approach applied to Advanced Very High Resolution Radiometer (AVHRR) observations in the thermal infrared region of the electromagnetic spectrum. The proposed approach, which exploits the analysis of multi-temporal satellite records, seems to be able to detect the anomalous signals on the sea due to the oil polluted areas with excellent reliability (0% of false alarms) and good sensitivity in different observational conditions. Its performances have also been evaluated in comparison with another well-known AVHRR approach, analysing the spill event which happened during the Gulf War off the Kuwait and Saudi Arabia coasts in January 1991. The results confirm the reliability of the proposed approach which promises to offer new economically sustainable opportunities for building a near-real-time monitoring system for oil spills on a global scale. Moreover, in order to further assess the exportability of the proposed approach in different observational and environmental conditions, outcomes obtained by applying it to the Seki–Baynunah event affecting the Gulf of Oman in March 1994 are also shown.     

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.     

Satellite monitoring of phytoplankton in the East Mediterranean Sea after the 2006 Lebanon oil spill

Multi-source remote-sensing data were used to study the aftermath of the 2006 Lebanon oil spill: Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data and advanced synthetic aperture radar (SAR) images were used to delineate the extent of the oil spill; QuikScat data were used to derive the wind field; and MODIS Ocean Colour data were used to demonstrate the variability of chlorophyll-a concentration (Chl-a) and sea surface temperature (SST). No significant changes were found in marine phytoplankton in the first few months after the oil spill. However, a big phytoplankton bloom was observed after 10 months, when Chl-a increased from 0.1 to 1.0 mg m^–3. After considering the time-series variability of Chl-a and SST and the spatial variability of Chl-a and bathymetry, we concluded that this phytoplankton bloom was probably related to the Lebanon oil spill.     

Dark formation detection using neural networks

Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in marine environment, as they are not affected by local weather conditions and cloudiness. Dark formations can be caused by man‐made actions (e.g. oil spills) or natural ocean phenomena (e.g. natural slicks and wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they dampen the capillary and short gravity sea waves. Thus, traditionally, dark formation detection is the first stage of the oil‐spill detection procedure and in most studies is performed manually or using a fixed size window in which a threshold value is adopted. In high‐resolution imagery, dark formation detection may fail due to the nonlinear behaviour of the pixel values contained in the dark formation and in the area around it. In this paper, we examine the ability of two feed‐forward neural network families, i.e. Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks, to detect dark formations in high‐resolution SAR images. The general objective of this paper is to test the potential of artificial neural networks for dark formation detection using SAR high‐resolution satellite images. Both the type and the architecture of the network are subjects of research. The inputs into the networks are the original SAR images. Each network is called to classify an area of the image as dark area or sea. The group of MLP networks can be recognized as the most suitable group for dark formation detection, as it presents reliable stable results for all the examined accuracies. Nevertheless, in terms of single topology, there is no an MLP topology that performs significantly better than the others.     

干涉合成孔径雷达海浪遥感研究

海浪是重要的海洋环境参数, 它对港口建设、海上航行、油气田开发等具有重要的意义。合成孔径雷达(Syn thet ic Apertu re Radar, SAR ) 是目前大范围测量海浪的主要工具, 用它来估计海浪主波系统的波长和传播方向比较好, 但要定量的提供海浪的振幅以及SAR 图像强度与实际海面的关系是相当困难的, 而且由SAR 图像获取海浪方向谱也比较复杂。沿航迹干涉合成孔径雷达(Along-Track Interferometric Synthet ic Aperture Radar,ATI-SAR ) 是在平台飞行方向上安置两根天线的双天线合成孔径雷达。相对于合成孔径雷达, 真实孔径雷达调制传递函数对沿航迹干涉合成孔径雷达相位图像的影响很小, 因此沿航迹干涉合成孔径雷达更适合测量海浪。详细介绍了沿航迹干涉合成孔径雷达海浪遥感的成像机制, 为进一步反演海浪方向谱奠定了基础, 最后针对目前研究存在的问题以及今后有待研究的方向进行了探讨。     

改进Notch滤波的全极化SAR数据船舶检测方法

全极化SAR数据提供了更多的地物极化散射信息,目前被广泛的应用于海上船舶检测的应用研究。本文提出改进的Notch滤波方法,实现全极化SAR数据的海上船舶检测。该方法结合目标的极化散射特性与能量双重特点,设计针对海面、方位向模糊、相干斑噪的不同滤波,消除虚警,通过SPAN能量因子降低由于散射机制相同而造成的漏检。利用RADATSAT-2全极化精细扫描数据对本文的算法进行验证,并与PWF和SPAN方法进行对比分析,实验结果表明本文提出的方法能从海面上有效检测出各种大小的船舶,同时能抑制方位向模糊、相干斑噪以及船舶的旁瓣造成的虚警。