基于纵横断面的机载雷达点云数据滤波     

黄迟  张汉德  范学炜  纪红如 《测绘科学》2015,(11):34-38

针对现有基于坡度滤波的改进方法难以同时提升滤波速度与精度的问题,文章提出了一种基于纵横断面的机载LiDAR点云数据滤波方法。该方法视测区为多个断面构成,以行(列)为滤波基本单位,兼顾高程突变的方向信息,分别从纵横断面以基于坡度的判据进行滤波处理,可同时提高计算速度与结果精度。实验结果表明,该算法计算速度快,并且对于不同特征的地形都能取得较好的滤波效果。  相似文献   

前后向平滑算法在动态精密单点定位中的应用     

门葆红  刘帅  孙付平  董文亮  徐海鑫 《测绘科学》2015,40(5)

针对PPP初始收敛段定位精度低而造成的定位结果无法利用的问题,文章提出将前后向平滑算法应用于动态PPP解算中,以获取整个观测时段的高精度定位结果;针对PPP成果缺乏质量检核手段的问题,文章提出构造前后向位置不符值作为质量检核指标。模拟动态算例和船载动态算例表明:平滑后的动态PPP克服了前向、后向滤波初始收敛段定位精度差的问题,整体位置误差RMS在10cm内,提高了数据利用率;收敛段的前后向位置不符值RMS也在10cm内,该指标可用来对PPP成果进行质量检核。  相似文献   

改进的中值滤波在图像去噪中的应用     

《地理空间信息》2015,(6)

阐述了中值滤波、软阀值法的去噪原理和优缺点,并用Matlab实现其算法。这2种方法在去噪时,会造成图像的部分细节丢失、边缘模糊等现象,针对这些问题,提出一种改进的中值滤波法。实验表明,改进后的中值滤波法具有更好的去噪效果。  相似文献   

震前电离层TEC异常探测方法的研究进展     

黄海莎  刘庆元 《测绘与空间地理信息》2015,(1)

介绍了震前电离层TEC异常探测原理的研究进展和主要的异常探测方法,详细介绍了时间序列法、Kalman滤波和小波变换在电离层异常探测中的原理和应用,总结了传统方法和新方法在电离层异常探测的应用发展情况,并分析了每种方法的优势与不足。为未来中国采用电离层异常探测开展地震预测工作提出了建议。  相似文献   

无人直升机激光扫描系统测图试验研究     

罗东山 《测绘技术装备》2015,(4)

本文阐述了自主研究的无人直升机激光扫描系统的构成,通过该系统在某试验区进行了实地飞行试验,通过控制三维激光扫描仪的扫描角和飞机的飞行高度来获取高精度的点云数据,对点云数据采用基于虚拟三角网和坡度分析的滤波算法进行了处理,并内插生成等高线图,对成果进行了精度检查.从分析得出生成的成果主要精度指标达到了1∶1000地形图的要求,可用于小面积地形测量.  相似文献   

各向异性组合滤波法反演陆地水储量变化   总被引：2，自引：1，他引：1  

超能芳  王正涛  孙健 《测绘学报》2015,44(2):174-182

地球时变重力场模型反演陆地水储量变化已为全球气候变化研究作出巨大贡献,考虑到时变重力场模型球谐系数中存在相关性,其高阶次项具有较大的误差,需采用最优的滤波方法进行空间平滑。本文提出一种新的各向异性组合滤波方法,其基本思想是将改进的高斯滤波法与均方根(root mean square,RMS)滤波法组合,即对球谐系数的低阶次采用改进的高斯滤波法,而高阶次采用RMS滤波法。首先分析了最新的GRACE RL05系列时变重力场模型系数误差特性,基于全球水储量变化反演结果,分析比较了高斯滤波、改进的高斯滤波、RMS滤波和DDK滤波与本文提出的组合滤波法的有效性及精度,并利用模型结果进行了验证,计算结果表明,组合滤波法的中误差最小。研究结果表明,本文提出的组合法相比于先前的滤波方法,可有效地过滤高阶次的噪声,消除南北条带误差,同时减少信号泄漏,提高信噪比,保留更多有效的地球物理信号,进而提高反演精度。  相似文献   

基于改进NAS-RIF算法的遥感噪声图像自适应复原     

张凡 《国土资源遥感》2015,(2):105-111

实现对遥感噪声图像的有效复原是遥感图像处理的一项重要研究内容。在对非负支撑域有限递归逆滤波(non-negativity and support constraints recursive inverse filtering,NAS-RIF)算法深入研究的基础上,提出一种基于改进自适应NAS-RIF算法的遥感噪声图像复原方法。该算法针对经典NAS-RIF算法存在的缺陷,首先对含有椒盐噪声和高斯白噪声的遥感图像采用自适应伪中值滤波算法进行预处理,以尽可能排除图像中噪声的干扰;然后结合图像的灰度值,从算法支撑域和背景灰度值2个方面加以改进;最后对代价函数引入基于目标信息的修正项,改进了经典NAS-RIF算法的代价函数;与对数函数复合,使得改进后NAS-RIF算法的代价函数具有良好的收敛性;并采用共轭梯度法对改进自适应NAS-RIF算法进行整体优化。对仿真实验结果进行的主观和客观分析表明,本文算法的性能优于经典NAS-RIF算法、已有的改进NAS-RIF算法以及小波阈值去噪方法,能够胜任遥感噪声图像的复原处理。  相似文献   

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution          下载免费PDF全文

XUE Ming  DONG Jili 《Acta Meteorologica Sinica》2013,27(3):379-399

Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.  相似文献   

Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA     

Jun Kyung KAY  Hyun Mee KIM  Young-Youn PARK  Joohyung SON 《大气科学进展》2013,30(5):1287-1302

Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.  相似文献   

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction     

刘征宇  武术  张绍晴  刘赟  容新尧 《大气科学进展》2013,30(5):1235-1248

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.  相似文献