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本文介绍了上杭县矿业权实地核查GPS控制网的布设方案,包括GPS控制网技术设计、外业观测、基线向量解算、控制网平差及精度分析和可靠性检验等。同时对GPS平面控制网建立的有关问题提出一些建议。 相似文献
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本文全面介绍了深圳地铁三号线GPS平面控制网的布设方案,包括GPS控制网技术设计、外业观测、基线向量解算、控制网平差及精度分析和可靠性检验等.同时对地铁GPS平面控制网建立的有关问题提出一些建议. 相似文献
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基于GPS静态相对测量,基线数据处理采用双差观测值单基线解算的一般模式,讨论GPS网基线解算和3维无约束平差的公式,得出GPS网的质量与基线向量质量和GPS网结构有关,而与网形和点位无关,并通过实验数据,分析验证了这一观点,提出了一些有益的结论。 相似文献
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在GPS事后相位差分定位模式中,基线处理的数学模型和整周模糊度的正确解算直接影响了坐标成果所能达到的精度指标。以土地资源调查和交通工程测量为工程实际应用背景,重点对GPS事后RTK坐标解算模型、GPS外业观测时间的合理确定和GPS工程控制网投影变形的处理三个方面进行了较为深入的研究。观测值权的确定是基线坐标解算过程所遇到的主要技术难点之一,在分析常规定权方法的基础上提出了“高度角定权法”,并以某大型水电站的实测数据分析了三种定权方法对基线坐标解算结果精度的影响,数据结果清楚地表明本文提出的“高度角定权”能有… 相似文献
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桥梁变形观测中GPS数据处理方法的研究 总被引：1，自引：0，他引：1
对桥梁变形体利用GPS进行变形监测,并通过GPS接收机随机软件进行外业观测数据的解算,研究GPS数据处理中解算基线向量时观测历元、截止高度角参数的最佳设置方法及平差模型的选择,以提高解算精度,为其他同类工程提供一定的参考. 相似文献
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对GPS控制网基线处理中对观测数据及基线解算质量评定要素进行了总结,针对GPS控制网内业数据处理基线解算中经常出现的一些问题,总结出优化解算的原则和方法,并提出合理建议。 相似文献
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黄金鱼 《测绘与空间地理信息》2013,(8):180-181,184
全球定位系统GPS是一种先进的测量工具,它的出现和不断发展,极大地促进了测绘工作的进步,具有快速、准确、高效的特点,而且不受地形条件的限制,大大提高了工程测绘的工作效率。本文结合南安市新农村D级GPS网的测量实例,阐述如何进行GPS控制网的技术设计、外业选点埋石、方案的观测实施、内业对观测数据的质量控制、基线解算、网平差计算以及精度分析等。同时对GPS平面控制网建立的有关问题提出一些建议。 相似文献
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《测绘与空间地理信息》2020,(6)
丽水市滩坑饮水隧道工程控制网由25个控制点组成,观测同步基线最远达到了58 km。本文采用GAMIT10.61版本解算工程控制网基线和CosaGPS V5.21进行网平差解算。结果表明:GAMIT解算的工程基线优于10-6,Cosa GPS平差整网形最弱边的基线相对精度为为1/9 528 000,ppm=0.10。 相似文献
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比较了广播星历和精密星历的差异,利用两种星历对某港区GPS控制网外业观测数据进行处理,分析了广播星历和精密星历对GPS网基线解算和网平差的影响。结果表明,常规短基线GPS控制网,完全可以用广播星历代替精密星历进行后处理解算,获取高精度的后处理结果。 相似文献
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在总结传统手工晕渲经验和方法的基础上，深入分析了计算机地貌晕渲的原理，着重研究了坡度和坡向对地貌晕渲中阴影强弱和明暗程度的影响，提出了变比例调整高程和晕渲笔调调整两种方法。实验表明，利用本文方法能有效改善地貌晕渲图的三维立体视觉效果。 相似文献
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基于GIS的城镇土地定级信息系统建模方法探讨 总被引：2，自引：0，他引：2
基于GIS的城镇土地定级信息系统在建设过程中,定级方法确定、定级单元大小确定和定级因素/子权重值的生成是关键,且三者都具有不同的确定方法,因此选择什么样的方法决定了系统建立的优劣。本文着重归纳、总结了城镇土地定级信息系统定级方法,通过对比分析,探讨了最优定级方法、最优网格单元大小划分方法和最优因素/子权重值计算方法。 相似文献
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通过模拟算例,比较了L曲线法、GCV法（广义交叉核实法）和虚拟观测法确定出的正则化参数,并对影响正则化参数的因素进行了分析,得出结论：正则化参数的确定与信噪比密切相关,当信噪比增大时,各种方法确定的正则化参数变化趋势不同,不同的情况确定正则化参数的适用方法也会有所差异。 相似文献
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Multicomponent Image Segmentation Using a Genetic Algorithm and Artificial Neural Network 总被引：1，自引：0，他引：1
Image segmentation is an essential process for image analysis. Several methods were developed to segment multicomponent images, and the success of these methods depends on several factors including (1) the characteristics of the acquired image and (2) the percentage of imperfections in the process of image acquisition. The majority of these methods require a priori knowledge, which is difficult to obtain. Furthermore, they assume the existence of models that can estimate its parameters and fit to the given data. However, such a parametric approach is not robust, and its performance is severely affected by the correctness of the utilized parametric model. In this letter, a new multicomponent image segmentation method is developed using a nonparametric unsupervised artificial neural network called Kohonen's self-organizing map (SOM) and hybrid genetic algorithm (HGA). SOM is used to detect the main features that are present in the image; then, HGA is used to cluster the image into homogeneous regions without any a priori knowledge. Experiments that are performed on different satellite images confirm the efficiency and robustness of the SOM-HGA method compared to the Iterative Self-Organizing DATA analysis technique (ISODATA). 相似文献
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Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping. 相似文献
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Johannes Bouman Sietse Rispens Thomas Gruber Radboud Koop Ernst Schrama Pieter Visser Carl Christian Tscherning Martin Veicherts 《Journal of Geodesy》2009,83(7):659-678
One of the products derived from the gravity field and steady-state ocean circulation explorer (GOCE) observations are the
gravity gradients. These gravity gradients are provided in the gradiometer reference frame (GRF) and are calibrated in-flight
using satellite shaking and star sensor data. To use these gravity gradients for application in Earth scienes and gravity
field analysis, additional preprocessing needs to be done, including corrections for temporal gravity field signals to isolate
the static gravity field part, screening for outliers, calibration by comparison with existing external gravity field information
and error assessment. The temporal gravity gradient corrections consist of tidal and nontidal corrections. These are all generally
below the gravity gradient error level, which is predicted to show a 1/f behaviour for low frequencies. In the outlier detection, the 1/f error is compensated for by subtracting a local median from the data, while the data error is assessed using the median absolute
deviation. The local median acts as a high-pass filter and it is robust as is the median absolute deviation. Three different
methods have been implemented for the calibration of the gravity gradients. All three methods use a high-pass filter to compensate
for the 1/f gravity gradient error. The baseline method uses state-of-the-art global gravity field models and the most accurate results
are obtained if star sensor misalignments are estimated along with the calibration parameters. A second calibration method
uses GOCE GPS data to estimate a low-degree gravity field model as well as gravity gradient scale factors. Both methods allow
to estimate gravity gradient scale factors down to the 10−3 level. The third calibration method uses high accurate terrestrial gravity data in selected regions to validate the gravity
gradient scale factors, focussing on the measurement band. Gravity gradient scale factors may be estimated down to the 10−2 level with this method. 相似文献
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GIS中折线元及面元置信水平的随机模拟 总被引：2，自引：1，他引：2
置信水平是描述GIS中线元素与面元素的位置不确定性的重要指标之一。文中应用MonteCarlo方法，利用C 中的rand()函数，经过转换，首先产生符合对点误差分布的节点坐标，再按照置信域的构建规则生成大量的随机区域，通过数值判断，算出区域覆盖要素真值的频率，以此频率作为置信水平的近似值，克服了用解析证明方法研究置信水平时结果往往过于保守这一缺陷。本模拟方法有较大的适用范围，为类似的置信水平研究提供了一般方法。 相似文献
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室内地标在室内位置信息服务（location based service,LBS）中发挥着重要作用。针对室外地标提取方法不能完全适用于更为复杂的室内环境的问题,提出了一种显著度定量评价模型用于在室内环境中提取地标。以大型商场的室内环境为研究对象,从视觉、认知、空间3个方面分析影响室内兴趣点（point of interest,POI）显著性的主要因素,并用这些因素构建了室内POI整体显著度评价模型。选择武汉市群光购物中心室内的POI数据进行显著度计算,依据显著度的差异性提取了多层地标,反映不同粒度的室内区域空间知识。提取的多层地标可以作为室内智能导航系统中的重要标识,为在复杂的大型商场内实现快速寻路、多粒度路径导引提供关键线索。 相似文献