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经典的渐进三角网滤波算法在LiDAR点云数据处理中应用十分广泛,但其滤波精度很大程度上取决于种子点选取的正确率。本文针对这一问题,在渐进三角网加密算法基础上提出了一种基于小格网高程、均方差和点密度统计数据选取种子点的迭代滤波算法。实验结果表明,本文的迭代滤波算法可有效避免低点等非地面点对种子点选取的干扰,且滤波结果生成的DEM精度较高,具有一定的实用性。 相似文献
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An adaptive spectral technique for ground clutter and noise suppression in weather radar echoes is presented. This technique detects weak echoes that are masked by the residuals from ground clutter. The technique is demonstrated on two clear air cases observed with Doppler weather radar. After adaptive suppression of ground clutter and its residue, features appear over the Oklahoma City urban area where otherwise none could be seen. These are interpreted as birds' corridors between two lakes and along a river. 相似文献
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《Geoscience and Remote Sensing Letters, IEEE》2009,6(2):268-272
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基于SPRT检验的并行递推次优Sage滤波器 总被引：2，自引：0，他引：2
提出了一种新的并行递推次优Sage滤波算法，在新方法中，设计了一种附加伴随滤波器的并行滤波结构，消除了结果常值偏差，并引入SPRT检验方法，通过检验模型的噪声统计是否发生了扰动，达到对噪声统计调整进行控制的目的，使得滤波器可以有效跟踪时变噪声，并减少了计算量。 相似文献
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针对现有LiDAR地面点滤波算法对复杂地形地物适应性不强的问题,本文提出了一种融合点云与地面影像分块滤波的方法。首先,将地面影像与点云匹配,使点云从影像中获取更多的光谱纹理信息。然后,分析地物光谱、林地相对密度、点云高程特征、地面DSM模型及其坡度,并基于决策级融合将原始点云切割成若干独立的区块。最后,根据每块区域不同的多元细节特征,对IPTD滤波算法进行改进并利用搜索法优化参数,得到最优且稳健的结果。利用滤波后的总地面点通过插值算法得到的DEM模型和相关试验验证了本文算法的优越性。 相似文献
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基于EMD-自适应滤波的干涉图去噪方法研究 总被引：1，自引：0，他引：1
提出了一种基于EMD-自适应滤波干涉图去噪方法,该方法基于信号和噪声经过经验模态分解后在不同的IMF上有不同的特征,即先对信号进行经验模态分解,然后对各个高频IMF信号分别选用不同的滤波梯度参数进行自适应滤波处理,从初始干涉图上减去与斑点噪声所对应尺度信息,从而达到噪声抑制的目的。通过实验对比研究了该算法与Goldstein滤波、圆周期中值滤波、EMD分解方法和梯度-自适应滤波去噪的降噪效果。实验表明,该方法不仅能有效地去除InSAR干涉图的噪声,并且能很好地保持相位的细节信息和条纹的边缘信息。 相似文献
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Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach 总被引：1，自引：0，他引：1
Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63–84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10–30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be connected to and employed in the first computational steps of the space-wise approach, where a time-wise Wiener filter is applied at the first stage of GOCE gravity gradient filtering. The results of this work can be extended to using other adaptive filtering algorithms, such as the recursive least-squares and recursive least-squares lattice filters. 相似文献
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李炎寅 《测绘与空间地理信息》2020,(1):89-92
针对自适应卡尔曼滤波只适用于滤除高斯分布的白噪声,本文提出了融合小波变换和自适应卡尔曼滤波的算法。该算法利用小波变换的多尺度分解,将GPS高频的监测时间序列进行多层分解,重构出新的GPS监测时间序列,将其作为新的自适应卡尔曼滤波初始值,进行滤波处理。将融合算法的滤波结果与单一的自适应卡尔曼滤波结果进行对比分析,结果表明融合算法的滤波效果较为显著。同时,对融合算法滤除的噪声信息进行统计分析,结果表明融合算法滤除的噪声符合正态分布,进一步说明了该融合算法的有效性,为GPS的高频率、高精度的监测提供了技术支持。 相似文献
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提出了一种三层组合滤波的去噪方法,在小波BayesShrink阈值与自适应中值滤波的基础上增加第三层Wiener滤波,利用Wiener滤波对信噪比高的信号去噪效果好的特点可有效去除残留的混合噪声,为了在去噪过程中保留影像的边缘,在滤波过程中加入了边缘提取算法,对影像的细节进行保留使去噪后的影像更加清晰。试验表明,本文提出的三层滤波方法在去除遥感影像常见的高斯与脉冲混合噪声时,效果要明显优于传统的两层组合滤波算法。 相似文献
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Assessment of terrain elevation derived from satellite laser altimetry over mountainous forest areas using airborne lidar data 总被引：1，自引：0，他引：1
Qi Chen 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(1):111-122
Gaussian decomposition has been used to extract terrain elevation from waveforms of the satellite lidar GLAS (Geoscience Laser Altimeter System), on board ICESat (Ice, Cloud, and land Elevation Satellite). The common assumption is that one of the extracted Gaussian peaks, especially the lowest one, corresponds to the ground. However, Gaussian decomposition is usually complicated due to the broadened signals from both terrain and objects above over sloped areas. It is a critical and pressing research issue to quantify and understand the correspondence between Gaussian peaks and ground elevation. This study uses ～2000 km2 airborne lidar data to assess the lowest two GLAS Gaussian peaks for terrain elevation estimation over mountainous forest areas in North Carolina. Airborne lidar data were used to extract not only ground elevation, but also terrain and canopy features such as slope and canopy height. Based on the analysis of a total of ～500 GLAS shots, it was found that (1) the lowest peak tends to underestimate ground elevation; terrain steepness (slope) and canopy height have the highest correlation with the underestimation, (2) the second to the lowest peak is, on average, closer to the ground elevation over mountainous forest areas, and (3) the stronger peak among the lowest two is closest to the ground for both open terrain and mountainous forest areas. It is expected that this assessment will shed light on future algorithm improvements and/or better use of the GLAS products for terrain elevation estimation. 相似文献
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残差χ2法难以准确检测小幅值突变故障,改进的序贯概率比检测(SPRT)方法通过迭代递推,能够检测到故障,但存在延时且对故障结束不敏感。基于此,本文给出了区分故障大小的方法,针对小幅值故障提出了一种基于衰减记忆思想的故障容错方法:当改进的SPRT方法检测到故障后,对系统进行隔离重构,用不包含故障信息的数据进行量测更新,利用衰减因数减小包含故障信息的陈旧滤波值的权重,以提高滤波精度,同时构造"伪正常"状态,使该方法能够及时判断故障结束。仿真结果表明,本文提出的方法适用于故障多发的情形,能够提高滤波精度,增强系统可靠性。 相似文献
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自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法 总被引：1，自引：1，他引：0
全色-多光谱影像融合技术可以显著提高遥感影像的地物判别能力,但是空间信息融入度与光谱信息保真度是相互矛盾的一组性质,一般方法往往无法平衡这两方面。SFIM算法具有良好的光谱信息保持能力,但是其空间信息融入度较差,影响了整体的融合效果。为此,本文分析了SFIM模型的原理与特点,提出一种自适应高斯滤波与SFIM模型相结合的全色多光谱影像融合方法（AGSFIM）。以均值调整后的多光谱整体平均梯度为标准来计算高斯滤波的最优参数,将下采样全色影像的清晰度调整至同样水平,以保证融合结果的空间信息融入度与光谱信息保真度之间的平衡。利用6种融合算法对“北京二号”（Beijing-2）、“资源三号”02星（ZY-3 02）数据进行对比试验,表明在良好的光谱保持能力的前提下,改进方法可以有效克服SFIM算法空间信息融入不足的缺点。 相似文献
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本文利用UTCSR 2003年1月到2008年8月间的GRACE Level-2 RL04重力场模型估计了南极冰盖质量变化。计算过程中分别采用高斯和Wiener滤波两种平滑方法,分别采用22、43和65个月重力场模型计算Wiener滤波信号与噪声函数,得出以下结论:在实际的计算过程中需要具体计算Wiener滤波平滑因子值,65个月GRACE重力场模型计算得到的Wiener滤波权值非常接近于平滑半径为540km高斯滤波权值;采用两种不同的滤波方法在相同区域质量变化率基本相同。 相似文献
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刘晓莉 《测绘与空间地理信息》2014,(8):89-91
使用GRACE反演地球表层的质量异常,其结果取决于构造的平滑函数即滤波方法。本文阐述了两种在经典高斯滤波基础上构造的Fan滤波和Non-isotropic Gaussian(Non)滤波,两者均对GRACE的阶和次起作用。在同一阶下,两者的权重随着次数的升高而衰减,但衰减的趋势不同,比如在20阶时,Fan滤波呈抛物线变化,而Non滤波则为线性衰减;在40和60阶,两者均呈抛物线变化。使用CSR发布的GRACE Level-2 RL05数据反演地表质量异常,两者的结果相近。 相似文献
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点云坡度滤波算法原理简单、易于实现,为进一步提升坡度滤波算法的自适应性,提出了一种多尺度自适应点云坡度滤波算法.首先,在数据预处理的基础上引入虚拟网格对点云数据进行分割;然后,利用距离加权的方式逐次计算网格点的坡度角,结合k均值聚类和正态分布自适应确定滤波阈值;最后,使用多尺度策略逐级缩小网格尺寸实现点云数据的精细滤波... 相似文献
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Adaptive estimation of multiple fading factors in Kalman filter for navigation applications 总被引：2，自引：1，他引：1
Kalman filter is the most frequently used algorithm in navigation applications. A conventional Kalman filter (CKF) assumes
that the statistics of the system noise are given. As long as the noise characteristics are correctly known, the filter will
produce optimal estimates for system states. However, the system noise characteristics are not always exactly known, leading
to degradation in filter performance. Under some extreme conditions, incorrectly specified system noise characteristics may
even cause instability and divergence. Many researchers have proposed to introduce a fading factor into the Kalman filtering
to keep the filter stable. Accordingly various adaptive Kalman filters are developed to estimate the fading factor. However,
the estimation of multiple fading factors is a very complicated, and yet still open problem. A new approach to adaptive estimation
of multiple fading factors in the Kalman filter for navigation applications is presented in this paper. The proposed approach
is based on the assumption that, under optimal estimation conditions, the residuals of the Kalman filter are Gaussian white
noises with a zero mean. The fading factors are computed and then applied to the predicted covariance matrix, along with the
statistical evaluation of the filter residuals using a Chi-square test. The approach is tested using both GPS standalone and
integrated GPS/INS navigation systems. The results show that the proposed approach can significantly improve the filter performance
and has the ability to restrain the filtering divergence even when system noise attributes are inaccurate. 相似文献