一种矢量瓦片零水印算法

针对栅格瓦片和矢量地图的零水印算法不能完全应用于矢量瓦片的问题,提出了一种基于色彩特征的SVG矢量瓦片零水印算法:以SVG矢量瓦片的色彩分布概率为重要特征,利用变换和特定的运算将该特征嵌入原始水印图像中形成零水印。由于该算法未对矢量瓦片数据进行任何修改,常见的几何编辑、几何变换、数据压缩、坐标精度约减等不影响水印的提取;因对各色彩特征进行了一定的约简,少量实体的删减也不影响水印的提取。实验结果表明该零水印算法对矢量瓦片生成效率的影响甚少,可以为矢量瓦片版权保护相关研究提供参考。     

图像2DRE四叉树—链式码向量的变换算法

本文叙述了图像数据结构的一种变换算法。近年发展起来的四叉树数据结构,是描述图像结构、压缩存储空间等方面的一种好方法;而链式码向量在检测和描述图像区域边界及其形状方面有很多长处。但两种结构各有其缺点,它们互相补充,才能得到较好效果。本文提出并分析了一个从2DRE四叉树得到图像中的区域边界的算法,即2DRE四叉树一链式码向量变换算法(简称Q—V算法)。文中介绍了算法的基础和算法本身,并对其特点和效果进行评价和分析。     

一种三维GIS矢量数据结构的研究--以矿山应用为例

本文针对传统数据结构的不足,借鉴二维矢量数据结构,并结合矿山实际情况,提出了用六组拓扑结构来刻划：点－结点,弧,环,曲面,体域和复杂地物之间的拓扑关系。引入“层次树”来动态维护这六组拓扑结构。最后针对一典型的矿山实体,给出了基于这六组拓扑结构的完整描述。实践证明：用本文所提出的六组拓扑结构能够很好地刻划出矿山实体的特点,“层次树”的引入则可大大减少数据维护所需的辅助操作。     

Introduced marine species: Management arrangements of consideration for the Torres Strait

Introduced marine species (IMS) can have significant impacts on economic, ecological, social and cultural aspects of coastal marine environments. There is, therefore, a need to minimise these impacts through the implementation of comprehensive and consistent management strategies and monitoring processes that work towards preventing introductions, detecting introductions if they were to occur and managing incursions should they be detected. There is also the need to ensure that approaches taken are comparable to provide consistency of IMS management effort, particularly across areas that are multi-jurisdictional (e.g. spanning State or Territory borders).     

基于ArcEngine的ArcGIS矢量数据到KML文件转换方法研究

ESRI是GIS领域的拓荒者和领导者，Google Earth则提供了全球范围的影像数据库，将二者结合起来进行商业开发具有很强的现实意义。本文介绍了基于ArcEngine的ArcGIS矢量数据到KML转换的主要思想，提出了转换方法，并进行了程序实现。     

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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。     

BP神经网络和SVM在矿山环境评价中的应用分析

矿山环境的影响因素多样,定量评价过程易受人为因素干预。BP神经网络与SVM算法能够自动模拟各因子间的非线性关系。首次将其引入到矿山环境评价中,选取160个单元作为训练样本,以自然地理、基础地质、开发占地及地质环境等4个大类的14个变量指标为输入向量,以单元评价得分为输出向量,分别建立BP神经网络与SVM矿山环境评价模型。结果表明：两种模型均能满足矿山环境评价的精度要求;SVM模型收敛速度较BP神经网络快,MSE小于BP神经网络,更适合矿山环境评价工作;将定量模型应用于研究区,评价得分划分为4个级别,与定性评价结果一致,为矿山环境评价工作提供了新思路。     

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.     

Classification and Recognition of Polyhalite in Chuanzhong Based on Support Vector Machine

Polyhalite is mainly solid mineral potassium in Sichuan Basin, The most of polyhalite layer in Sichuan region is impurity, and usually accompanied by layers of gypsum, anhydrite, rock salt, and even deposited in the same layer. Conventional logging interpretation method can only roughly identificate polyhalite layers. Based on the theory of Support Vector Machine and logging interpretation methods,this paper creates prediction model with the input of logging curves, and discriminates the polyhalite reservoirs in the lower-middle Triassic strata. Compared with logging data, the accuracy rate of the discrimination results reaches 90%. According to the prediction model, identification model can be established with the curve features of polyhalite to discriminate pure polyhalite reservoirs, gypsiferous polyhalite reservoirs and polyhalite-gypsum reservoirs, the accuracy rate is 91.78%. The study demonstrates that Support Vector Machine is superior to the method of logging interpretation, and it has broad prospects in potash exploration.