基于移动激光扫描点云特征图像和SVM的建筑物立面半自动提取方法

建筑物立面是城市地物的重要组成部分,而移动激光扫描是获取城市地物三维信息的重要手段之一。本文提出了一种基于移动激光扫描点云的建筑物立面半自动提取算法。该方法首先构建研究区水平网格;然后计算局部点云几何特征,并且将特征投影到水平网格生成点云特征图像;接着基于支持向量机（Support Vector Machine,SVM）对建筑物立面网格进行粗提取;最后使用网格属性（形状系数、网格面积、最大高程）对粗提取结果进行过滤,并将结果反投影到三维空间中得到精确的建筑物立面。以卡内基梅隆大学的移动激光扫描点云进行试验后表明,本算法能够较好地提取出建筑物立面,提取精度为84%,召回率为90%,数据修正后精度为88%,召回率为91%。通过与现有算法对比,本文提出的算法具有较高精度。     

A practical formulation for estimating the extreme vector excursion of a floating structure

In the design of a moored floating structure, the traditional approach is to assess the extreme surge and sway responses separately. This paper highlights the importance of considering the extreme lateral excursion, based on the vector sum of the surge and sway offsets, and an approximate formula is developed for this purpose. The method allows for first- and second-order platform motions, as well as mean offsets. The extreme vector excursion is expressed as a simple function of the maximum surge and sway responses, and the correlation coefficient between the two motions. The proposed approximation is benchmarked against more rigorous methods, including time domain simulation. It is found that provided the estimates for the surge and sway extremes are precise, the formula itself does not induce significant errors in the extreme vector excursion. Moreover, it is observed that the prediction tends to be conservative. The practicality of the proposed formulation makes it amenable for incorporation into an existing design code or analysis procedure.     

Comparing the Kalman filter with a Monte Carlo-based artificial neural network in the INS/GPS vector gravimetric system

Rigorous physical and mathematical analysis has been intensively developed to obtain the gravity disturbance vector from the inertial navigation system and the global positioning system. However, the combination of the observation noise and the systematic INS errors make it very challenging to accurately and efficiently describe the dynamics of the system with rigorous equations. Thus, the accuracy of the gravity disturbance estimates, especially in the horizontal components, is limited by the insufficient error models. To overcome the difficulty of directly modeling the systematic errors with exact mathematical equations, a Monte Carlo based artificial neural network is successfully applied in the moving base gravimetric system. The computation results show significant improvement in the precision of all components of the gravity disturbance estimates.     

一种贝叶斯和支持向量机相结合的相关反馈策略

相关反馈技术在提高图像检索性能方面发挥着重要作用,但图像检索过程中的相关反馈存在反馈次数过多,反馈效果不够理想等问题。为解决上述问题,提出一种贝叶斯和支持向量机相结合的反馈算法。实现方法是:用贝叶斯分类器对图像库进行分类,达到压缩图像库的目的,然后用支持向量机分类器对压缩之后的图像库进行分类,并反馈最终结果。研究结果表明,与支持向量机和贝叶斯算法相比,在很少的反馈次数下,该方法明显提高了反馈效果。     

支持向量机方法在郑州冬季气温趋势预测中试应用

将气候预测中常用的74项环流特征量进行归一化处理后,与郑州市冬季气温进行相关普查,利用SVM(SupportVectorM ach ine)两类分类方法,同时考虑气温的年代际变化,建立郑州冬季温度距平趋势预测推理模型,并对因子个数多少和年代际变化对预测模型的影响进行了试验。试验结果表明:用25个和15个因子分别建模时,产生最优模型时样本平均Ts评分均为56%,但后者预报准确率为75%,较前者提高了10%。用20世纪50年代和60年代做试验集,效果较好,产生最优模型时的样本Ts评分和预报准确率较高;用90年代做试验集,效果较差。     

考虑地应力的洞室围岩块体稳定性分析的理论与实践

地下洞室围岩的稳定性状态，在很大程度上受围岩应力状态决定，以前的地下洞室岩稳定性分析，一般仅考虑在自重作用下的稳定性状态。而吻略了地应力地围地应力对围岩稳定性的影响，所得结论往往偏于保守。     

不同降维策略下的高光谱影像多特征分类

由于物体表面的空间分布通常是富有规律且局部连续的,在高光谱影像分类中应充分利用其光谱和空间信息.本文在对高光谱影像立方体进行降维处理的基础上,提出了一种联合空域和谱域信息的高光谱影像高效分类方法.首先,分别选用主成分分析(Principal Component Analysis,PCA)和正交投影波段选择(Orthog...     

基于支持向量机模式识别的大雾预报方法

选取1971～2000年11～12月大雾发生前近地面层的气象要素(气温、降水、能见度、风向风速、相对湿度、云量等9个预报因子),将支持向量机(SVM)方法应用于大雾预报.采用支持向量机方法,应用径向基函数,建立了陕西公路站点大雾24 h预报模型,并进行了大雾预报的模拟、训练,其寻优标准TS评分达到了理想的效果.     

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per- formance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91％ for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05％ using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.     

支持向量机方法在单站降水预报中的应用探讨

将武汉天空云量预报的81个预报因子运用到该站中等以上强度的降水预报中,基于SVM方法进行了交叉验证和预报试验。结果表明用81个预报因子建立的5～9月和全样本的降水预报模型有较好稳定性、且对降水都有正的预报技巧。因此天空云量的预报因子可以用来做降水的预报因子,同时也证明了这些预报因子在天空云量和降水预报中是协调的。SVM方法为天空云量和降水的预报提供了客观参考依据。