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51.
Polarimetric synthetic aperture radar(SAR) oil spill detection parameters conformity coefficient(μ), Muller matrix parameters(|C|, B_0), the eigenvalues of simplified coherency matrix(λ_(nos)) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena(oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches(low wind and biogenic slick) with the signal below the noise,polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes(low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration. 相似文献
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Wind plays an important role in hydrodynamic processes such as the expansion of Changjiang （Yangtze） River Diluted Water （CDW）, and shelf circulation in the Changjiang estuary. Thus, it is essential to include wind in the numerical simulation of these phenomena. Synthetic aperture radar （SAR） with high resolution and wide spatial coverage is valuable for measuring spatially inhomogeneous ocean surface wind fields. We have collected 87 ERS-2 SAR images with wind-induced streaks that cover the Cbangjiang coastal area, to verify and improve the validity of wind direction retrieval using the 2D fast Fourier transform method. We then used these wind directions as inputs to derive SAR wind speeds using the C-band model. To demonstrate the applicability of the algorithms, we validated the SAR-retrieved wind fields using QuikSCAT measurements and the atmospheric Weather Research Forecasting model. In general, we found good agreement between the datasets, indicating the reliability and applicability of SAR- retrieved algorithms under different atmospheric conditions. We investigated the main error sources of this process, and conducted sensitivity analyses to estimate the wind speed errors caused by the effect of speckle, uncertainties in wind direction, and inaccuracies in the normalized radar cross section. Finally, we used the SAR-retrieved wind fields to simulate the salinity distribution off the Changjiang estuary. The findings of this study will be valuable for wind resource assessment and the development of future numerical ocean models based on SAR images. 相似文献
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差分InSAR处理及其应用分析 总被引：5，自引：0，他引：5
本文结合实例分析和介绍了影响In SAR处理结果的几个重要环节。如: 数据源及像对选择 ;影响In SAR处理的因素等。目前有4～5种雷达卫星数据可以用于In SAR的处理,ERS-1/2卫星数据效果最好; 差分In SAR处理的工作步骤中,亚像元精度的配准、相干性分析、全相位恢复等是其中的关键技术。In SAR的精度和噪声水平是In SAR能否实用的重要条件,噪声影响地物的相干性,噪声较大部位相干性消失,在整幅图像中噪声区域不大于30% ,将不影响整体的In SAR测量结果。 相似文献
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在高分辨率机载干涉SAR成像处理过程中,由于载机飞行过程中偏离理想轨迹,需要高精度的惯导系统和GPS系统记录载机的运动轨迹并进行运动补偿。然而,由于目前传感器导航精度的限制,在完成运动补偿处理后仍然存在轨道误差,从而影响干涉相位的精度,本文提出了一种机载双极化InSAR轨道误差去除方法。该方法利用小波多尺度分析对不同极化方式差分干涉相位进行多尺度分解,减弱地形误差相位、噪声相位对轨道误差的干扰,然后根据不同极化方式轨道误差的高度相关性,对相位进行降权改正,得到轨道误差改正后的差分干涉相位。为验证该方法的可靠性,分别采用模拟数据和E-SAR P波段双极化数据进行实验分析,结果表明原始差分干涉图中的轨道误差剔除明显,利用P波段获得的校正干涉图生成数字高程模型(DEM),轨道误差改正前后获取的DEM与LiDAR值的均方根误差(RMSE)分别为6.02 m和1.68 m,提高了InSAR测高精度。本研究为机载InSAR轨道误差补偿提供了一种新的思路。 相似文献
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基于SAR干涉点目标分析技术的城市地表形变监测 总被引：1，自引：0，他引：1
通过深入研究干涉点目标的相位模型,提出基于空间搜索的邻近点目标干涉相位差解缠方法,用以计算点目标的地形残差和线性形变,以及分离点目标大气延迟相位和非线性形变相位的时空域滤波方法,解决干涉点目标分析中的关键问题.最后,以苏州地区地表沉降监测为应用试验,利用形成的SAR干涉点目标形变信息提取技术,获取苏州市区1992-2002年间的地表沉降信息.研究结果与已有文献记录保持比较好的一致性,证明SAR干涉点目标技术完全可以发展成为应用于城市地表形变监测的实用化技术.Abstract： Interferometric point target analysis (IPTA) is one of the latest developments in radar interferometric processing. It is achieved by analysis of the interferometric phases of some individual point targets, which are discrete and present temporarily stable backscattering characteristics, in long temporal series of interferometric SAR images. This paper analyzes the interferometric phase model of point targets, and then addresses two key issues within IPTA process. Firstly, a spatial searching method is proposed to unwrap the interferometric phase difference between two neighboring point targets. The height residual error and linear deformation rate of each point target can then be calculated, when a global reference point with known height correction and deformation history is chosen. Secondly, a spatial-temporal filtering scheme is proposed to further separate the atmosphere phase and nonlinear deformation phase from the residual interferometric phase. Finally, an experiment of the developed IPTA methodology is conducted over Suzhou urban area. Totally 38 ERS-1/2 SAR scenes are analyzed, and the deformation information over 3 546 point targets in the time span of 1992-2002 are generated. The IPTA-derivecl deformation shows very good agreement with the published result, which demonstrates that the IPTA technique can be developed into an operational tool to map the ground subsidence over urban area. 相似文献
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SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery. 相似文献
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