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Oil spill detection with fully polarimetric UAVSAR data 总被引：3，自引：0，他引：3
In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle – synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case. 相似文献
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Ground deformation measurements have contributed to a better understanding of the processes and mechanisms involved in natural hazards. Those include landslides, subsidence, earthquakes and volcanic eruptions. Spaceborne Differential Interferometric Synthetic Aperture RADAR (DInSAR) is a well studied technique for measuring ground deformation. Quality of deformation measurements, however, is often degraded by decorrelation. With the advent of fully polarimetric SAR satellite sensors, polarimetric optimization techniques exploiting polarimetric diversity improve the phase quality of interferograms. In this paper, we analyzed three polarimetric optimization methods to determine the optimal one for application in an arid natural environment. We considered coherence decomposition in single and double phase center scenarios. Coherence estimation bias associated with each optimization method has been analyzed. We compared the derived displacement values with terrestrial GPS measurements. The study shows that polarimetric optimization increases the number of coherent pixels by upto 6.89% as compared with a single polarization channel. The study concludes that polarimetric optimization coupled with DInSAR analysis yields more reliable deformation results in a low coherence region. 相似文献
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湖南地热资源的构造制约与勘查标志 总被引：1，自引：0，他引：1
针对湖南省境内地热资源的区带分布特征，分析了NE向或NNE向花垣－永顺－桑植断裂、靖县－溆浦断裂、新宁－娄底－宁乡断裂、连云山－双牌断裂、临武－郴州断裂、汝城－桂东断裂对区域地热资源分布的控制作用。地热资源的分布主要受区域主干断裂控制，局限于主干断裂上盘的一定范围内，其赋水岩层主要为古生界灰石和花岗岩破碎带，单个热泉的产出距主干断裂20－40km，热泉温度分布不仅与赋水围岩有关，而且与热泉产出部位的构造强度关系密切，其中构造强度较大的湘西北及湘东南地区以中温热泉为特征，而在构造强度较小的湘东及湘西南地 区则以低温热泉为主。在 综合地热分布相关的构造控制因素前提下，提出了省内寻找地热资源的构造标志。 相似文献
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This paper describes a novel technique which determines the co-polarization channel imbalance by the use of natural bare soil, instead of a trihedral corner reflector (CR). In polarimetric synthetic aperture radar (PolSAR) remote sensing, the polarimetric calibration (PolCAL) is the key technique in quantitative earth parameter measurement. In general, the current PolCAL process can be separated into two parts. The first part tries to estimate the crosstalk and the cross-polarization (x-pol) channel imbalance components by the reflection symmetry and the reciprocity properties, without a CR. Then, at least one trihedral CR is required to determine the co-polarization (co-pol) channel imbalance; however, it is not always possible to deploy a CR in difficult terrain such as desert. In this paper, we utilize bare soil as a stable reference target, and four common natural constraints of bare soil are evaluated to determine the co-pol channel imbalance, without the use of a CR. It should be mentioned that we do not propose to replace the CR by a natural target, but we utilize the natural target to enhance the PolCAL accuracy when a CR is missing. The four constraints are: (1) the consistency of the polarimetric orientation angle (CPOA) between the PolSAR POA and the digital elevation model (DEM) derived POA; (2) the unitary zero POA (UZPOA) of a flat ground surface; (3) the zero helix (ZHEX) component of the ground surface; and (4) the unitary version of the previous zero helix (UZHEX). In the theoretical part of this paper, we demonstrate that the forth constraint is the most suitable in different scenes. We then propose a multi-scale algorithm to further improve the robustness of the co-pol channel imbalance determination. In the experimental part, we apply our new methods to simulated airborne SAR (AIRSAR) and real uninhabited aerial vehicle SAR (UAVSAR) data. Without the use of any CR, the recovered results show that the estimated amplitude and phase error of the co-pol channel imbalance are less than 0.5 dB and 5°, respectively. 相似文献
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复数域最小二乘平差及其在POLInSAR植被高反演中的应用 总被引：2，自引：1，他引：1
传统的测量观测值都是实数,因此测量平差都是在实数空间中进行的。然而,随着科学技术的快速发展,现代测绘领域中出现了一些用复数表示的观测数据。与实数数据一样,这些复数数据同样面临着如何从带有误差的观测值中找出未知量的最佳估计值的问题。但目前涉及复数观测的数据处理时,主要还是依据观测过程,分步或直接解算,不能考虑观测误差、多余观测信息等。针对这一情况,本文介绍了复数域中数据处理的最小二乘方法,试图将测量平差从实数域推广到复数域,并定量研究了两种平差准则的优劣性。为了了解复数域最小二乘的有效性,本文以极化干涉SAR植被高反演为例,建立复数域平差函数模型和随机模型,构建复数域最小二乘法反演植被高。结果表明该算法反演的植被高结果可靠,其精度优于经典植被高反演算法,且计算简单,易于实现。 相似文献
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Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment 总被引：3，自引：0，他引：3
This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented. 相似文献
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In this paper, the linear discriminative Laplacian eigenmaps (LDLE) dimensionality reduction (DR) algorithm is introduced to C-band polarimetric synthetic aperture radar (PolSAR) agricultural classification. A collection of homogenous areas of the same crop class usually presents physical parameter variation, such as the biomass and soil moisture. Furthermore, the local incidence angle also impacts a lot on the same crop category when the vegetation layer is penetrable with C-band radar. We name this phenomenon as the “observed variation of the same category” (OVSC). The most common PolSAR features, e.g., the Freeman–Durden and Cloude–Pottier decompositions, show an inadequate performance with OVSC. In our research, more than 40 coherent and incoherent PolSAR decomposition models are stacked into the high-dimensionality feature cube to describe the various physical parameters. The LDLE algorithm is then performed on the observed feature cube, with the aim of simultaneously pushing the local samples of the same category closer to each other, as well as maximizing the distance between local samples of different categories in the learnt subspace. Finally, the classification result is obtained by nearest neighbor (NN) or Wishart classification in the reduced feature space. In the simulation experiment, eight crop blocks are picked to generate a test patch from the 1991 Airborne Synthetic Aperture Radar (AIRSAR) C-band fully polarimetric data from of Flevoland test site. Locality preserving projections (LPP) and principal component analysis (PCA) are then utilized to evaluate the DR results of the proposed method. The classification results show that LDLE can distinguish the influence of the physical parameters and achieve a 99% overall accuracy, which is better than LPP (97%), PCA (88%), NN (89%), and Wishart (88%). In the real data experiment, the Chinese Hailaer nationalized farm RadarSat2 PolSAR test set is used, and the classification accuracy is around 94%, which is again better than LPP (90%), PCA (88%), NN (89%), and Wishart (85%). Both experiments suggest that the LDLE algorithm is an effective way of relieving the OVSC phenomenon. 相似文献
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2019年8月16日诸城超级单体风暴双偏振参量结构特征分析 总被引：5，自引：2，他引：3
利用青岛S波段双偏振多普勒天气雷达探测资料和常规气象观测资料以及区域自动气象站观测资料,对2019年8月16日发生在山东省诸城市的一次长寿命超级单体风暴双偏振结构特征进行了分析。结果表明:超级单体风暴发生在东北冷涡和地面中尺度辐合线共同作用背景之下,对流有效位能偏低,但风垂直切变非常强,这种配置有利于超级单体风暴的形成与维持。诸城超级单体风暴持续3 h左右并伴有深厚持久的中气旋,旺盛阶段最强反射率因子、基于单体的垂直累积液态含水量、强中心高度和单体顶部高度平均分别为74.1 dBz、67.9 kg/m2、6.3 km和11.3 km。对偏振特征分析表明,风暴低层60 dBz以上回波区对应偏小的差分反射率（Zdr）、小的相关系数（CC）和大的差分相移率（Kdp）,湿（或干）冰雹和液态雨滴共存。此外,低层入流缺口附近有明显Zdr弧存在。风暴中层强上升气流区内有明显的有界弱回波区,其顶部达到7 km左右。有界弱回波区内相关系数较小,其周围有明显的Zdr环和CC环,Zdr环顶部达到?10℃层高度。0℃层高度之上存在深厚的Zdr柱和Kdp柱,顶部都达到?20℃层高度,Zdr柱位于有界弱回波区东侧,Kdp柱位于西侧。?10—?20℃层,Zdr柱对应强的水平极化反射率因子（35—60 dBz）和小的差分相移率,表明含有少数偏大的液态或湿冰粒子,而Kdp柱对应更强的水平极化反射率因子（55—72 dBz）和小的差分反射率,表明含有一定数量的小的液态或（和）湿冰粒子及大的冰雹粒子。风暴低层强反射率核后侧径向上如果出现显著差分反射率负值区,可作为特大冰雹（直径≥50 mm）的识别依据;如果对应异常大的差分相移率,表明含有浓度较高的雨滴和包有水膜的冰雹粒子。 相似文献
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本文统计分析了北京地区近三年的有效降水,重点研究了积层混合云降水特点并对其分类,发现积层混合云降水出现频次约占总降水次数的61%,其中积层混合云降水以积层连结型和水平混合型为主,二者之和占近80%。重点分析了积层混合云中对流和层云两种不同特点降水类型的宏微观结构,确立了反射率因子Z、温度T、粒子含水量M、催化剂AgⅠ（碘化银）活化率NE和粒子相态HTC（hydrometeor type classification）为人工增雨潜力识别指标及这些识别指标的取值范围,同时也根据研究现状和人工影响天气需求总结制定出人工增雨潜力等级。利用偏振雷达构建模糊逻辑识别算法对积层混合云三种降水类型进行增雨潜力区域识别研究,结果表明:（1）对于播撒碘化银增雨来说,积层混合云的增雨潜力区在垂直方向上可分为上、中、下三层,上层（增雨等级为“不适合”）和下层（零度层及以下）分别受含水量和温度等影响不适合增雨,中间层（增雨等级大于等于“等级一”）是可增雨区域;（2）积层混合云中层云区增雨潜力较小,对流云区可增雨潜力要远大于层云区,开式流场型与积层连结型可增雨潜力要大于水平混合型;（3）当降水云中识别出霰粒子时,其附近的大部分区域会有较好的增雨潜力。通过偏振雷达实例检验和数值模式模拟在积层混合云不同部位播撒碘化银催化试验发现,在增雨潜力较好的区域催化有很明显增雨效果,模拟试验结论与偏振雷达识别增雨潜力区结果也基本一致,说明基于偏振雷达的增雨潜力区识别方法和结果是具有参考意义的。 相似文献