基于极化相干层析技术的林分高度估算

极化相干层析(Polarization Coherence Tomography,PCT)技术可以通过不同极化状态下的干涉相干系数反演植被垂直结构分布,是SAR技术应用的一个重要发展方向。文中介绍极化相干层析的原理,总结极化相干层析技术重建垂直剖面的流程,通过仿真数据和ALOS PALSAR数据对极化相干层析技术在林分高度估算方面的适用性进行研究。估算结果表明,极化相干层析技术反演的林分高度特征信息准确,具有较强的鲁棒性。     

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。      

极化方位角补偿信息支持下的植被参数反演

SAR图像中散射目标的散射矩阵受极化方位角(POA)的影响会改变散射体的散射特性,散射矩阵是极化干涉SAR (PolInSAR)估计不同极化状态下复相干性的基础。本文根据极化方位角产生机制,建立了多视情况下基于极化方位角补偿的极化干涉相干性估计模型,分析了极化方位角补偿对相干性估计方法和不同散射机制下相干性估计的影响程度,研究了基于三阶段法与极化方位角补偿的植被参数反演。利用L波段SIRC全极化SAR图像为实验数据验证极化方位角补偿对极化干涉相干性估计和植被参数反演的可行性。实验结果表明,极化方位角补偿能够改变不同极化状态相干性分布规律,提高相干直线拟合精度,改善植被参数反演的可靠性和合理性。     

基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究

极化SAR影像中阴影、水体和裸露的耕地3种地物类型有非常相似的极化散射特性,常规基于非相干分解的分类方法难以将其有效地区分。对此,本文引入基于Freeman分解的散射熵Hf和各向异性度Af两个特征参数,并将其用于极化SAR影像分类。首先利用Hf和Af参数将阴影和水体提取出来,然后将其他地物按散射机制分为3大类,并对每一类再次利用Hf和Af参数进行细分,最后通过基于Wishart分布的聚类和迭代分类,得到最终的分类结果。通过利用Radarsat-2在河南登封获取的全极化SAR数据进行试验,表明该算法执行效率高,能够有效地区分阴影、水体和裸露的耕地,并且对其他地物类型也有很好的分类效果。     

极化干涉SAR反演植被垂直结构剖面研究

植被的高度和垂直结构剖面都是森林生物量和森林碳循环模型中的关键输入参数,极化干涉SAR的出现使定量获取植被结构参数成为可能。首先,将改进的两次拟合法估计的植被高度作为进一步反演植被垂直结构剖面的先验知识;然后,通过勒让德多项式展开以及双基线极化干涉数据提取植被的垂直结构剖面;最后,利用仿真以及真实极化干涉SAR数据进行方法验证和结果定性分析。研究表明,利用不同极化状态下的干涉相干性变化提取植被结构参数是可行且有效的。     

层析SAR反演森林垂直结构参数现状及发展趋势

森林垂直结构参数反演是进行森林资源管理、森林蓄积量估算及全球碳循环研究的基础。层析合成孔径雷达TomoSAR(Tomography Synthetic Aperture Radar)是随着InSAR/Pol-InSAR技术的日益发展而产生的,更适用于森林垂直结构参数反演。本文首先介绍了TomoSAR的概念与实现方式:PCT(Polarization Coherence Tomography)、多基线干涉层析SAR MB-InTomoSAR(Multi-baseline Interferometric Tomographic SAR)、多基线极化层析SAR MBPolTomoSAR(Multi-baseline Polarization Tomographic SAR);概括了目前应用TomoSAR技术反演森林垂直结构参数的技术方法与信号模型等;论述了应用TomoSAR技术提取森林垂直结构参数的现状,最后分析了应用TomoSAR技术提取森林垂直结构参数可能的发展方向。     

基于功率和相位联合估计TLS-ESPRIT算法的极化干涉SAR数据分析

在推导各散射信号功率计算方法的基础上,提出基于功率和干涉相位联合估计的TLS-ESPRIT方法。因为干涉图像中的各主散射波和干涉相位能被分别估计,所以该算法可以利用同一分辨单元中不同散射机制后向散射信号功率的不同,将干涉相位分类实现散射中心分离。该方法通过提取两个占优势的相位得到两个高度,从而分别检测出森林的植被层和地表,特别是当散射波不相关并有不同的极化特性时,该方法有较好的性能,从而提高植被高度参数的估计精度。详细阐述该方法的原理和实施步骤,并通过对SIR-C/X-SAR的L波段实际数据进行分析处理,验证算法的有效性。     

一种自适应的混合Freeman/Eigenvalue极化分解模型

全极化SAR数据的极化分解在土地利用分类、目标检测与识别以及地表参数反演等领域得到了广泛应用。目前,主要有基于特征值分解和基于模型分解2类极化分解方法。混合Freeman/Eigenvalue极化分解结合了两者的优势,避免了基于模型的极化分解中负功率问题并且能够利用已知的散射机制解释分解后的散射分量。为了进一步拓展该分解在不同地表类型中的应用,通过引入参数Neumann一般化体散射模型,提出了一种自适应的极化分解模型。利用德国Black Forest地区的L波段AirSAR(airborne synthetic aperture Radar)全极化数据进行实验,并与现有的Yamaguchi三分量模型和自适应非负分解(adaptive nonnegative eigenvalue decomposition,ANNED)对比分析,以验证模型的有效性。研究表明,自适应的混合Freeman/Eigenvalue极化分解模型保证了分解能量的非负性及完全分解,适应于不同类型的地表,能有效地区分不同地类。     

玉米作物极化SAR数据模拟

通过重点研究玉米叶子的空间位置、指向角分布,分析玉米作物的结构特点,建立玉米的三维结构模型和场景,发展针对玉米作物的极化合成孔径雷达(synthetic apertureradar,SAR)数据模拟方法。利用该模拟方法和实地获取的玉米参数模拟极化SAR数据,通过与散射计实地测量的多极化、多角度后向散射系数进行对比,表明该模拟方法能够有效的模拟玉米作物的后向散射系数;通过分析模拟极化SAR数据获得的HH-VV、HH-HV、VH-VV之间的相位差,表明该模拟方法能够有效的模拟玉米作物散射的相位信息;通过分析模拟数据的极化响应图和Cloude H-α分类图,从散射类型角度验证了模拟极化SAR数据的有效性。     

一种利用Cloude-Pottier分解和极化白化滤波的全极化SAR图像分类算法

提出了一种新的基于Cloude-Pottier分解和极化白化滤波(PWF)的全极化SAR数据分类算法。该算法利用PWF的结果来代替反熵A对复WishartH/α分类结果进行进一步细化,按PWF的值将复WishartH/α分类结果由8类分为16类,然后再次进行Wishart迭代分类。实验结果表明,该算法能有效地提高分类精度,分类结果明显优于常规的复WishartH/α分类结果和复WishartH/α/A分类结果。     

Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring

Monitoring crop condition using optical satellite indices has a legacy of several decades. Early warning of variances in crop production is vital in mitigating regional and global food insecurity. Adoption of optical vegetation indices for this purpose is widespread, yet cloud cover impedes the acquisition of these data. Although early research using scatterometers and aircraft hinted at the sensitivity of Synthetic Aperture Radar (SAR) responses to crop development, the implementation of satellite SAR observations in operational crop condition monitoring is limited. In the research presented here, volume-to-surface (V/S) scattering ratios derived from C-band RADARSAT-2 quad and simulated compact polarimetric (QP and CP) imagery are assessed for their potential to monitor crop growth. Both V/S ratios were strongly correlated with optical vegetation indices, including the widely adopted Normalized Difference and Soil Adjusted Vegetation Indices. The changes in the ratio of volume to surface scattering were correlated with variations in crop biomass. The results support the potential of a SAR scattering ratio for crop condition monitoring. In particular, encouraging results were reported for compact polarimetry, a mode that can be implemented to deliver broader swath coverage conducive to regional and national monitoring.     

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%.     

一种扩展的基于结构检测的最优多视极化SAR相干斑滤波算法

全极化数据的可得,使得利用极化散射矩阵以减少多视全极化SAR图像中的相干斑成为了可能。其中,被称为最优的多视极化白化滤波器（MPWF）就是一个典型例子。然而,为提高滤波器参数估计的精度,同时能够自适应的检测图像中的结构特征,在滤波时采用适当的窗算法是必要的。本文提出了两个有效的窗算法,并用1994年NASA SIR-C/X-SAR,L band,经过四视处理的天山森林的极化数据进行了仿真。实验结果表明,这两种算法不仅很好的达到了降噪效果,而且对图像的纹理信息以及结构特征（即边缘特征、线性特征、强散射体特征）的保持效果是最佳的。     

自交叉双边滤波的极化SAR数据相干斑抑制

相干斑抑制是极化SAR数据预处理的关键步骤。双边滤波是一种空域和值域滤波相结合的优秀边缘保持滤波算法。针对双边滤波在抑制极化SAR数据相干斑的不足,该文将改进的交叉双边滤波引入到极化SAR数据降噪领域,加入散射机制测度来扩展原权重核,根据SPAN图像的局域变差系数自动调整空间方差系数,利用参考图像来度量灰度值和散射机制相似性。实验结果表明:本文方法较经典滤波算法有更强的噪声平滑能力和更好的细节信息保持能力,在保持原数据极化散射信息方面也表现出良好的性能,这为基于极化SAR数据的后续应用提供了支持。     

On the degree of polarization for SAR sea oil slick observation

A polarimetric model to relate the degree of polarization, DoP, to the sea surface scattering with and without oil slicks, under low-to-moderate wind conditions, is proposed. DoP, measured directly from the Mueller scattering matrix, is shown to be a reliable measure of the departure from Bragg scattering; a phenomenon that, under low-to-moderate wind conditions, occurs when an oil slick is present. Following this theoretical rationale, a simple filter is developed to observe oil slicks in quad-polarimetric full-resolution Synthetic Aperture Radar (SAR) data. Experiments, undertaken on a meaningful set of quad-polarization Single Look Complex (SLC) C-band RADARSAT-2 SAR data, where both well-known oil slicks and a weak-damping look-alike are in place, demonstrate the soundness of the model and its effectiveness from an operational viewpoint.     

Slicks detection on the sea surface based upon polarimetric SAR data

This letter proposes a generalized-likelihood ratio test-based edge detector to be fed by possibly polarimetric synthetic aperture radar (SAR) data. It can be used to detect dark spots on the sea surface and, hence, as the first stage of a system for identification and monitoring of oil spills. The proposed constant false alarm rate (CFAR) detector does not require secondary data (namely pixels from a slick-free area); remarkably, a preliminary performance assessment, carried out by resorting to real SAR recordings, shows that it guarantees detection capabilities comparable to those of previously proposed polarimetric CFAR detectors (which though make use of secondary data). The preliminary performance assessment also seems to indicate that processing polarimetric data does not ensure improved detection capabilities.     

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.     

相向飞行全极化SAR图像对建筑物的立体重构

用多方向飞行的全极化SAR图像可能提取特定三维目标的高度与位置信息,进而实现目标物的几何立体重构。全极化SAR图像数据与单极化SAR相比,可以选择多种极化组合数据,提供对于特定目标几何特征敏感的数据类型,通过多方向飞行SAR图像反演该目标或目标群的高度与位置信息。本文用两幅相向飞行的PI-SAR(日本机载极化与干涉SAR,X波段、1.5m分辨率)图像,提取日本仙台电视塔高度、日本东北大学建筑物群的立体重构。