遥感影像目视解译矢量化分析

遥感技术已成为快速、客观、准确监测土地利用变化状况及其变化规律的重要手段.在土地调查工作中,具有十分重要的研究意义和使用价值,也是十分可行的一种技术手段.文章通过第二次土调查遥感影像解译及矢量化实际应用,阐述了遥感影像在二调中的应用,论证了遥感影像在第二次土地调查应用中的方法,得出了在土地调查工作中遥感影像的应用具有十...     

基于切平面投影的散乱数据点快速曲面重建算法

介绍了一种快速的散乱数据点曲面重建算法(切面投影三角网法),该算法不需要已知数据点的几何、拓扑信息以及是否存在边界等先验知识。算法利用邻近点集反映出的局部几何和拓扑信息,基于切平面投影方式计算每个数据点的邻域,从而完成每个数据点的局部拓扑重构。重构中物体表面数据点的降采样或不均匀采样可能会产生伪洞,因此,在重建后进行洞的检测,进而根据洞的大小来区分物体表面上实际存在的洞和重构过程中生成的伪洞,并对伪洞进行填充。利用多组散乱数据点进行重建的结果表明,切面投影三角网法高效、稳定,可以快速、自动地重构出复杂拓扑结构物体的三角网表面模型。     

贵州喀斯特山区矢量数据栅格化最佳尺度选择

针对GIS空间数据尺度效应问题,通过对贵州喀斯特山区的矢量数据栅格化最佳尺度选择研究,提出研究对象的复杂性、空间分析与制图的精度要求和实际运算效率是影响尺度选择的最主要因素,进行多要素、综合性的空间分析时,25 m～30m的栅格数据是最合适的转换尺度。     

三维拓扑数据的生成算法与交互修改方法研究

在分析现有三维矢量拓扑关系的基础上,提出了三维矢量数据结构的要素拓扑模型（Feature Topological Mod—el,FTM）。FTM三维拓扑模型比传统三维模型的优势有3点：（1）直接引入三角形对象,更加有利于表示任意形状的三维体数据。（2）简化基本拓扑关系的定义,使用相对简单的三角形和边的拓扑关系,表达相对复杂的实体和曲线的拓扑关系;使用带拓扑关系的边的集合表示三维曲线,既表达了曲线对象,又不影响基本拓扑关系的维护。（3）使用实体之间的包含模型,解决环（洞）的拓扑关系。同时,使用FTM对三维离散三角形数据进行拓扑化处理,对其中存在的拓扑错误提出具体的解决方案。该拓扑模型成功应用于自主研发的三维勘查软件“探矿者”中。     

薄互层砂岩油藏矢量布井方式

薄互层砂岩油藏具有纵向厚度大、单层控制储量规模小、储层非均质性强等地质特点,常规面积井网布井方式面临储量控制程度低、层间矛盾突出、采油速度低以及经济可行性差等问题,导致油藏整体开发效果较差。立足于含油砂体的储层沉积学成因特征,基于多孔介质渗流理论建立单砂体尺度的矢量井网布井方法,推导单砂体平面非均质矢量井网井距的设计公式,并根据井距设计公式构建物理模型开展平板模型水驱油实验,应用油藏数值模拟方法对油藏尺度矢量井网部署进行应用,形成薄互层砂岩油藏砂体叠置区域与非叠置区域的层系组合及布井方式的差异化开发技术对策。     

Relevance vector machine applied to settlement of shallow foundation on cohesionless soils

The settlement of shallow foundation on cohesionless soil is a key parameter in the design of shallow foundation. The recently introduced relevance vector machine (RVM) technique is applied to predict the settlement of shallow foundation on cohesionless soils. RVM allows computation of the prediction intervals, taking into account the uncertainties of both the parameters and the data. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. It also estimates the prediction variance. This study shows that compared to the available methods, RVM is better at determining the settlement of shallow foundation on cohesionless soil.     

Water vapor motion signal extraction from FY-2E longwave infrared window images for cloud-free regions: The temporal difference technique

The aim of this study is to calculate the low-level atmospheric motion vectors （AMVs） in clear areas with FY-2E IR2 window （11.59-12.79 μm） channel imagery,where the traditional cloud motion wind technique fails.A new tracer selection procedure,which we call the temporal difference technique,is demonstrated in this paper.This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature （TB） differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions.The TB difference corresponding to a 10％ change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission （MODTRAN4） radiative transfer model.The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions：tropical,midlatitude summer,U.S.standard,and midlatitude winter.The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB ＂wind＂.This technique is valid over cloudfree ocean areas.The proposed algorithm exhibits encouraging statistical results in terms of vector difference （VD）,speed bias （BIAS）,mean vector difference （MVD）,standard deviation （SD）,and root-mean-square error （RMSE）,when compared with the wind field of NCEP reanalysis data and rawinsonde observations.     

基于OLI影像多参数设置的SVM分类研究

在遥感影像自动分类中仅使用光谱特征很难产生正确的分类,OLI影像是波段数较多的多光谱影像,如果增加纹理、几何等多种特征以提高分类精度,就会使得特征的维度很高.支持向量机善于解决小样本、非线性和高维的影像分类问题,但是核函数和参数的设置只能依靠实验来获得.文中在OLI影像中提取了23个特征,逐个测试核函数和参数值对分类结果的影响.研究的主要结论如下：RBF核的支持向量机分类精度最高,Sigmoid核支持向量机分类精度最低;核函数的选择对分类精度的影响最大;核函数和参数值的变化不会影响重要特征的使用,3种核的支持向量机分类所使用的重要特征基本一致.     

一种改进的GNSS／INS非相干深组合导航

针对GNSS／INS非相干深组合导航中通道滤波器状态模型中信号幅值估测不准而导致跟踪环路观测噪声较大的问题,提出了一种改进的GNSS／INS非相干深组合导航方法。该方法通过去除通道滤波器中信号幅值状态变量,增加载波相位变化率加速度误差,重新设计了通道滤波器数学模型。仿真结果表明：相比于传统的非相干深组合导航方法,这种滤波器的跟踪性能与导航精度有了进一步提高。     

Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels

The prospect of regular assessments of insect defoliation using remote sensing technologies has increased in recent years through advances in the understanding of the spectral reflectance properties of vegetation. The aim of the present study was to evaluate the ability of the red edge channel of Rapideye imagery to discriminate different levels of insect defoliation in an African savanna by comparing the results of obtained from two classifiers. Random Forest and Support vector machine classification algorithms were applied using different sets of spectral analysis involving the red edge band. Results show that the integration of information from red edge increases classification accuracy of insect defoliation levels in all analysis performed in the study. For instance, when all the 5 bands of Rapideye imagery were used for classification, the overall accuracies increases about 19% and 21% for SVM and RF, respectively, as opposed to when the red edge channel was excluded. We also found out that the normalized difference red-edge index yielded a better accuracy result than normalized difference vegetation index. We conclude that the red-edge channel of relatively affordable and readily available high-resolution multispectral satellite data such as Rapideye has the potential to considerably improve insect defoliation classification especially in sub-Saharan Africa where data availability is limited.