海洋一号C星光学载荷对海面溢油的识别能力分析

中国首颗海洋水色业务卫星(HY-1C)搭载有水色水温扫描仪(COCTS)、海岸带成像仪(CZI)、紫外成像仪(UVI)等多套光学载荷，已于2019-06开展业务化运行。溢油是海洋环境监测的重要目标之一。近年来，光学遥感对海面溢油不同污染类型的识别、分类与估算原理得以阐明，其技术优势获得认可，能有效促进海面溢油的精细化定量探测。本研究以中国近海，主要以2019-02-20中国南海东沙岛附近海域一次溢油事件为例，收集了HY-1C星COCTS和CZI数据，并获取了同一天的VIIRS、MODIS Terra & Aqua遥感数据，开展了海面溢油的识别分类及其对比分析，重点评估COCTS与CZI光学载荷对海面溢油识别的效能。结果表明：在弱耀光对比条件下，COCTS、CZI、VIIRS均能有效识别此次东沙岛溢油，且COCTS对溢油的识别能力不弱于VIIRS数据；CZI不仅能识别此次溢油，还能区分此次溢油形成的海面油膜与溢油乳化物，从而提供更多的判定信息；CZI数据中，不同溢油污染类型的光谱信号离散度大于清洁海水平均噪声，展现了溢油量估算的潜力。HY-1C星光学载荷展现了优良的辐射分辨率、高信噪比等性能，随着HY-1D星的发射，双星组网观测还能提供更佳的时相分辨率数据，未来会在海洋溢油的实时、定量、精细化观测中提供丰富的数据支撑。     

基于机载多光谱遥感数据的溢油信息提取方法

为研究应用紫外-可见光-近红外-热红外遥感数据识别海上溢油的方法,利用机载多光谱溢油监测设备获取了舟山海域多光谱遥感数据,并利用野外地物光谱仪获取了水体和油膜的反射光谱。通过提取并分析机载多光谱遥感图像上多目标的影像特征和光谱响应特征,利用决策树分类法对海上油膜相对厚度的分布进行分类和制图。研究表明,基于机载多光谱遥感数据的溢油信息提取方法总体分类精度达93.7%,能够准确区分薄油膜和厚油膜,可有效提取海洋溢油污染信息,完全能够满足海洋溢油污染遥感监测需求。     

GNSS反射信号海面溢油回波DDM仿真研究

海上溢油已成为影响海洋生态环境的重要污染物之一,我国近40年发生约3200起海上溢油事故。当今用于监测海上溢油的遥感主要是光学和雷达卫星,卫星遥感往往重访周期长,而海上溢油事件时常发生,给海洋带来严重的环境污染,需要快速、准确的监测其状况。GNSS R技术具有全天候监测海洋的特点,因此更适合用于海面溢油检测。为了验证GNSS R技术在检测海面溢油的可行性,利用2013年中国青岛海洋溢油事故的遥感图像的溢油结果,作为仿真实验检测目标,进行岸基的GNSS R海面溢油检测仿真研究。利用Z V散射模型和海水／溢油的均方坡度(MSS)模型结合,建立了能反映海面状况GNSS散射信号特征的时延 多普勒图(DDM)。仿真得到DDM中检测到海面溢油区域,验证了利用GNSS反射信号进行海面溢油检测的可行性。      

烃类微渗漏现象遥感检测研究进展

油气藏烃类微渗漏是一种普遍现象,利用遥感技术的快速、经济及高效等优势,探测烃类微渗漏从而发现油气田,是当前很多油气公司采用的主要勘探方法之一。介绍了烃类微渗漏的理论基础,阐述了烃类微渗漏在陆地表面、海底近地表及海洋表面的多种表现形式,综合论述了我国在应用遥感技术探测烃渗漏现象方面的研究进展,展望了遥感技术在未来烃渗漏检测方面的发展趋势。     

基于RadarSAT-2同步观测试验的海面油膜雷达信号特征研究

海底自然烃渗漏在海洋表面形成的油膜分布对海上油气勘探具有重要指示意义。本文利用3 m分辨率的RadarSAT-2数据在渤海海域开展海洋表面油膜检测同步现场试验,利用机油在海面生成表面油膜,14 min后接收SAR数据,测量并分析海洋表面油膜雷达响应特征。试验结果表明,在适合海况条件下,3 m分辨率SAR数据可以检测到几个微米厚度的表面油膜,此时C波段对海洋表面波有5 dB的阻尼作用。油膜漂移的SAR测量结果进一步证实表面油膜的扩散与漂移受风场、流场影响。     

柴达木盆地烃蚀变矿物高光谱遥感识别研究

高光谱遥感识别烃蚀变矿物可用于探测油气烃类微渗漏和定位地下油气藏.以有天然气分布的柴达木盆地东部三湖地区为研究区,对Hyperion高光谱数据进行重采样处理,克服了目标识别矿物不明显和传感器低信噪比的影响.通过确定烃蚀变矿物高光谱遥感探测的指示标志,采用线性光谱(SAM)拟合与光谱匹配(SAM)相结合的方法确定了影像端元对应的矿物组分.识别结果表明,合理缩减影像波段数和确定影像端元的方法,能有效提高烃蚀变矿物的高光谱遥感识别精度.     

遥感监测墨西哥湾溢油目标识别算法

为了及时监测溢油事故相关信息,防止污染扩张,该文利用2010年4月29日、5月17日及5月24日3个时相的中分辨率MODIS数据,以2010年发生的墨西哥湾溢油事故为例进行目标提取与识别。提取算法考虑到油膜和背景海水易混分的现象,采用了辅加纹理特征量的光谱角匹配算法进行油膜提取;同时,对比了常用的最小距离和支持向量机分类方法;并基于混淆矩阵对结果进行精度验证。结果显示,改进的光谱角匹配算法可以更准确地提取海面目标,精度高达90%以上,能够较好地监测出大面积溢油,可以用于海面溢油灾害的动态监测。     

基于HJ–CCD数据的海面溢油提取方法研究

快速、准确地获取溢油污染信息,对海洋的动态监测、保护和可持续利用具有重要意义。环境与灾害监测预报小卫星星座一号(HJ–1)是我国针对生态环境污染和灾害监测发射的新型卫星平台,但HJ–1 CCD多光谱数据的光谱波段较少,仅依赖光谱信息获取海面溢油范围的精度较低。因此,以墨西哥湾溢油事件为研究对象,在分析不同地物光谱特征的基础上,采用灰度共生矩阵,选择合适的纹理结构因子,提取HJ–1 CCD图像中影响溢油识别的地物纹理特征;建立光谱特征和纹理特征相结合的决策树模型,提取海面溢油信息,并与只考虑光谱信息的传统分类方法进行精度对比。结果表明,与最大似然分类法相比,决策树方法的油膜提取用户精度和制图精度分别提高了11.85%和4.28%。     

世界卫星海洋遥感三十年

全面扼要地介绍30a来世界卫星海洋遥感所取得的主要成就及主要海洋卫星探测器的技术性能,重点介绍海洋遥感——海洋水色、海面地形、海面风场以及波浪、溢油和海冰监测等方面的技术进步和应用研究进展。     

水色遥感中偏振信息的研究进展

介绍了目前水色遥感中,水面反射光的偏振特性及其应用与水体散射光的偏振特性及其应用的研究进展.着重分析了基于偏振原理的水面反射光的剥离、海面溢油偏振遥感探测与基于水体物质偏振信息的水质参数反演等几个问题的研究现状;总结了在水色遥感信息流中,大气偏振校正、气-水界面偏振描述、基于偏振水质遥感反演、传感器偏振响应标定等各个环节入射光与出射光的偏振改变与响应机制等几个关键问题.     

Automated Approach for Extraction of Oil Spill from SAR Imagery

Oil spill pollution is a major environmental concern since it has most dangerous and hazardous effects on marine environment. Periodic monitoring by detecting oil spills along with its movement, helps in efficient clean-up and recovery operations. Over the past few years, Synthetic Aperture Radar (SAR) based remote sensing has received considerable attention for monitoring and detecting oil spill due to its unique capabilities to provide wide-area observation in all weather conditions. However, the interpretation of marine SAR imagery is often ambiguous, since it is difficult to separate oil spill from look-alike features. The objective behind our study was to extract probable oil spill candidates automatically from SAR imageries containing oil spill incidences, where new methods based on over-segmentation and amalgamate approach is used for this purpose. The methodology is all about over segmenting the entire image based on its statistics and amalgamating relevant segments at later point of time to represent actual dark features as probable oil spill candidates. Under the dependency on SAR imageries alone, the approach does not take care of the separation of look-alike features which can be addressed subsequently by the consideration of associated synchronous external data resources such as optical data, wind and ocean parameters (Zhao et al. in Opt Express 22(11):13755–13772, 2014; Espedal and Wahl in Int J Remote Sens 20(1):49–65, 1999). The approach is carried out on a set of RISAT-1 imagery containing oil spill incidences and the extracted oil spill areas are in well agreement with the visually interpreted output with kappa coefficient greater than 0.70 and overall classification accuracy greater than 80%.     

简缩极化特征值分析的溢油检测

为有效利用简缩极化SAR进行海洋溢油检测,本文基于简缩极化特征值分析,提出了3个用于简缩极化溢油检测的参数,引入了基于简缩极化特征值分解的简缩极化熵Hc(Compact Polarization Entropy)、简缩极化比参数PFc(Compact Polarization Fraction)、简缩极化基准高度PHc(Compact Polarization Pedestal Height)特征进行海洋溢油检测。海表的散射类型主要为低熵散射(小粗糙面发生的Bragg散射),为弱去极化、弱散射过程随机性状态,由于溢油会阻尼海水的Bragg散射,使其熵值变高、呈去极化、强散射过程随机性状态,故简缩极化熵、简缩极化比参数和简缩极化基准高度可以用来检测海洋溢油。本文采用C波段的Radarsat-2、SIR-C/X-SAR数据进行了实验,结果表明:简缩极化熵、简缩极化比参数和简缩极化基准高度能够有效抑制疑似溢油,使海水与疑似溢油差异变小;突出溢油区域,使海水与溢油的可区分性变大。     

SAR图像不同油膜特征的最佳分割方法选择

SAR图像溢油分割是SAR溢油监测中一个重要环节。文中选取4种不同形状、尺寸和对比度的SAR油膜数据,分别采用双峰阈值分割法、最大熵分割法、区域生长法、分水岭算法、图割法、水平集方法等6种方法进行溢油信息提取,探讨适合于不同油膜特征的最佳提取方法。结合现有尺度分割标准,提出一种SAR图像溢油信息评价指数——有效分割指数(Effective Segmentation Index,ESI),对不同分割方法得到的溢油提取结果进行定量评价,得出了不同特征油膜所适合的最佳分割方法。     

Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes

Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection.     

Simple Approaches to Oil Spill Detection Using Sentinel Application Platform (SNAP)-Ocean Application Tools and Texture Analysis: A Comparative Study

Effective and efficient monitoring of oil spills that originate from ships, offshore platforms and any accidents are of immense importance from the viewpoint of public safety and environmental protection. Detection of spilled oil is also essential to estimate the potential spread and drift from the source to the nearby coastal areas. In this regard, utilization of SAR data for the detection and monitoring of oil spills has received considerable attention in recent times, due to their wide area coverage, day-night and all-weather capabilities. In this paper, two oil spills incidents along the coast of Mumbai, India are investigated; (1) The 2010 oil spill that occurred after the MV MSC Chitra and MV Khalijia-3 collided and (2) the oil spill caused due to sinking of MV RAK carrier in 2011. Two simple and relatively quick approaches for oil spill detection have been applied to VV polarized Radarsat-2 imagery of the incidents and a comparison is made of the results obtained. The first approach utilizes the oil spill detection tool of Sentinel Application Platform (SNAP) and the second explores texture analysis using Grey Level co-occurrence matrix (GLCM). The results of the study show that texture analysis proves to be an efficient method for oil spill detection as compared to the SNAP oil spill detection tool. Nevertheless, both the proposed methodologies are useful for detection of oil spills and for subsequent utilization of the results, timely and cost effectively, for the calibration and validation of numerical models that predict oil spill dispersion trajectories.     

油气勘探中的化探遥感综合评价技术

油气化探和油气遥感虽然是两种不同的油气勘查方法,但二者在油气勘查中具有相辅相成的关系。地表化探异常与遥感影像异常的空间分布具有一致性,综合评价化探异常与遥感异常的油气指示意义,可提高油气勘探效率。     

Marine Radar Oil Spill Monitoring Technology Based on Dual-Threshold and C–V Level Set Methods

Marine radar oil spill monitoring technology has the advantages of wide-range target detection, a flexible working mode, and the ability to operate in severe weather. Based on oil spill data collected from the 7.16 accident in Dalian, China, two comprehensive oil spill monitoring methods using X-band horizontal polarized marine radar are presented. The improved Prewitt operator, Otsu algorithm, median filtering, and mean filtering were used to preprocess the original marine radar image. Subsequently, the recognition of offshore oil films were studied via the dual-threshold method and C–V level set method. Results show that the image data achieved the ideal noise reduction effect after pretreatment, and retained marine radar ocean wave information effectively. The C–V level set method required relatively lower levels of data preprocessing. However, it relied heavily on the energy-driven window by expert presupposition. It was therefore only suitable for obtaining continuous oil film information. Its calculation efficiency was related to the number of evolutions and the size of the image. The dual-threshold method required higher data preprocessing. Furthermore, the effective monitoring range of ocean waves must be determined in advance. This method could identify discrete oil film information at sea and had high computational efficiency. The dual-threshold method is better for the automatic identification of marine oil film information. The two kinds of monitoring methods described herein are useful for the identification and disposal of oil spills at sea.     

面向地震应急调查的遥感应用现状及趋势分析

地震是一种会造成人类生命财产重大损失的突发性自然灾害,震后第一时间启动应急响应并开展灾情的快速评估能有效地减轻地震灾害带来的破坏。空间对地观测技术为宏观性的地震应急与调查工作提供了便捷、经济的途径,随着空间对地观测技术与数据处理技术的不断发展,各国学者对遥感应急调查开展了大量深入的研究工作,相关研究成果已广泛地应用于地震应急的实际工作中。但是,遥感数据类型与处理技术的多样化也带来了应急信息的散乱,导致遥感快速应急响应系统性不强,使得应急服务不持续,一定程度上限制了遥感技术的效能;为此,针对现阶段遥感技术在地震应急调查中的应用情况,在总结地震应急调查常用遥感技术手段的基础上,分析了遥感快速应急响应面临的技术挑战,重点梳理了地震应急不同阶段对遥感数据及应急专题产品产出类型与时效性的现实需求,结合震后灾区影像数据的情况,系统地分析了光学、雷达、激光雷达遥感技术在地震应急调查应用中的技术现状与存在的问题。在实际地震应用案例分析的基础,总结剖析遥感应急工作存在的问题,并重点从海量数据快速处理、震害信息智能化提取、多源数据协同分析3个技术层面论述了遥感地震应急面临的核心困难,基于此,结合在轨数据实时、海量数据快速处理就灾情智能化识别的多技术联合、多源数据协同分析、发展敏捷卫星等几个方面论述了未来遥感技术在防震减灾中的发展趋势,以期推动遥感监测手段提供动态、实时、持续的空间信息应急服务能力,提高地震应急工作的快速响应、精细化与业务化应用能力。本文的研究可以为多源遥感技术在地震应急调查中的科研及业务应用提供很好的参考,更高效的发挥遥感技术在防震减灾工作中的应用能力与水平。     

使用多尺度模糊融合的高分影像变化检测

为了提高高分辨率遥感影像变化检测的可靠性,提出了一种基于模糊综合评判的遥感影像变化检测方法.首先对两个时相的影像进行波段叠加,对多波段新影像进行多尺度分割;然后针对单一尺度上的对象,综合考虑两时相遥感影像对象的光谱特征和纹理特征,建立模糊综合评判模型,对各个对象内的像素是否发生变化进行隶属度计算;最后采用熵权法对影像各...