A metaheuristic approach for efficient and effective sketch-to-metric map alignment

Sketching as a natural mode for human communication and creative processes presents opportunities for improving human–computer interaction in geospatial information systems. However, to use a sketch map as user input, it must be localized within the underlying spatial data set of the information system, the base metric map. This can be achieved by a matching process called qualitative map alignment in which qualitative spatial representations of the two input maps are used to establish correspondences between each sketched object and one or more objects in the metric map. The challenge is that, to the best of our knowledge, no method for matching qualitative spatial representations suggested so far is applicable in realistic scenarios due to excessively long runtimes, incorrect algorithm design or the inability to use more than one spatial aspect at a time. We address these challenges with a metaheuristic algorithm which uses novel data structures to match qualitative spatial representations of a pair of maps. We present the design, data structures and performance evaluation of the algorithm using real-world sketch and metric maps as well as on synthetic data. Our algorithm is novel in two main aspects. Firstly, it employs a novel system of matrices known as local compatibility matrices, which facilitate the computation of estimates for the future size of a partial alignment and allow several types of constraints to be used at the same time. Secondly, the heuristic it computes has a higher accuracy than the state-of-the-art heuristic for this task, yet requires less computation. Our algorithm is also a general method for matching labelled graphs, a special case of which is the one involving complete graphs whose edges are labelled with spatial relations. The results of our evaluation demonstrate practical runtime performance and high solution quality.     

基于相似特征点集的SIFT匹配改进算法

当影像中存在相似或重复场景时,传统SIFT匹配算法存在匹配成功率低,目前改进的SIFT匹配算法计算量大。基于相似特征点集的SIFT匹配改进算法,依据相似性或重复场景的影像纹理特点,在SIFT特征点匹配过程中,通过设定阈值提取初始同名点,建立针对未成功匹配参考特征点的相似特征点集,利用已获取初始同名点建立仿射几何约束模型构建参考特征点的匹配约束窗口,在该窗口内利用特征点相对主方向及尺度约束,对特征相似点集进行匹配获得同名点,最后采用RANSAC算法剔除误匹配点。对比实验结果表明,在影像像对间存在较多相似性场景,同时存在较大尺度缩放、旋转变换、视角及模糊差异的情况下,文中算法在匹配成功率和计算复杂度上具有明显的优势。     

附有长度约束的模糊度解算方法比较

整周模糊度解算方法的合理选择是GNSS定向处理中的关键问题,针对此问题,该文对GNSS定向中的LAMBDA_C和CLAMBDA两种附有基线长约束的模糊度固定方法的性能进行了详细的对比分析。结果显示,相对于经典的基于Ratio检验的LAMBDA算法,LAMBDA_C和CLAMBDA均可提高模糊度固定率和正确率,尤其是在单频单系统下提高效果更为显著;CLAMBD的总体固定效果略优于LAMBDA_C,但后者的解算效率更高。     

有向拓扑的河系渐变自动绘制算法

河系渐变绘制是地图制图中水系要素表达的重要方面。针对全自动河系渐变研究较少的现状,该文提出一种基于树状约束stroke特征的河系渐变自动绘制算法。该方法在深入分析有向拓扑的数据结构基础上,重点解决河口模式识别、树状约束stroke特征构建、河系层次关系建立、主支流分级渐变等问题,实现全自动河系渐变,并进行实践测试。结果表明,该算法在地图制图过程中无须对河系河源、河口识别、河系分层和河系渐变进行人工干预,自动化程度高,大幅度提升制图效率,渐变效果优于人工绘制效果。     

基于GIS的甘肃省农业生产潜力研究

在气象数据库、属性数据库和GIS支持下,采用机制法对作物生产潜力模型进行了光、温、水、土逐级订正,得到了甘肃省的光合、光温、降水、水资源及土地的生产潜力,其结果很好的反映了甘肃省农业生产和农业资源分布的空间格局,各级订正的有效系数进一步揭示了各种资源因子对农业生产的限制程度。     

煤心损失气量计算方法在页岩气中应用的适用性分析

煤心损失气量计算方法在工程条件、煤心性质等方面均有约束条件和适用范围,探讨该方法在页岩气中应用的适用性尤为重要。分析认为,该方法约束条件和适用范围的提出,主要是基于煤层气赋存状态的多样性以及不同煤心损失气、解吸气和残余气占比的差异性。煤层气赋存受控于煤层演化程度及结构,损失气、解吸气和残余气占比与工程条件密切相关。页岩与煤相比,在岩石性质、组成、孔隙结构、比表面特征等方面存在较大差异,加之页岩岩心与煤心钻取条件的不同,决定了煤心损失气量计算方法应用于页岩气的适用条件是:页岩中微孔占有明显优势;比表面积大、具有很强的气体吸附能力;页岩气赋存状态以吸附态为主;提心过程中逸散气量小。     

解决物探异常解释多解性的一次尝试

以利用物探资料寻找隐伏铁矿的工作为例,通过对某地区重磁电十字剖面资料的推断解释,总结了减少物探异常解释多解性的思路与做法,提出在布置外业工作和收集资料时,就应考虑为减少多解性取全取准所需的各类用作约束的资料,认为采用逐一判断—排除的方法是可选途径。     

育种进化的改进遗传算法在水电站负荷分配中的应用

针对遗传算法求解水电站负荷优化分配问题时常出现的收敛性差、易早熟等问题,提出一种基于育种进化的改进遗传算法。改进算法运用部分解约束的初始种群生成法避开空蚀振动区,定义了与群体进化程度有关的种群多样性函数和种群多样性阈值,同时有效地应用了遗传的全局搜索能力和育种的局部搜索能力。以三峡水电站为例与标准遗传算法进行了比较,不同的负荷分配结果表明:育种进化的改进遗传算法能够避开空蚀振动区的影响,保证机组的稳定安全运行;同时由于育种进化的强局部搜索能力,保持了种群的多样性,提高了算法的搜索能力和收敛性。     

大别超高压变质过程中部分熔融作用的地球化学约束

报道了大别超高压变质带西部麻城四道河榴辉岩-围岩剖面系统的元素和同位素地球化学研究成果, 对超高压变质榴辉岩在俯冲和折返过程中的部分熔融作用进行了探讨. 研究表明, 榴辉岩原岩性质类似于N-MORB, 其长英质围岩可分为TTG片麻岩和含石榴石花岗岩; 两类长英质围岩Sm -Nd同位素特征与其寄主的榴辉岩相似; REE特征、w (Nb)/w (Ta) 比值、Nd同位素组成及锆石U -Pb定年等地球化学证据支持了TTG片麻岩为大别地区陆壳俯冲过程中发生部分熔融作用而形成, 其与超高压榴辉岩的关系属特殊的异地关系; 含榴花岗岩为超高压榴辉岩折返过程中的部分熔融作用形成, 但因其形成环境为石榴石稳定场的深度, 故含榴花岗岩与超高压榴辉岩被视为近似原地的构造关系.      

A geostatistical basis for spatial weighting in multivariate classification

Earth scientists and land managers often wish to group sampling sites that are both similar with respect to their properties and near to one another on the ground. This paper outlines the geostatistical rationale for such spatial grouping and describes a multivariate procedure to implement it. Sample variograms are calculated from the original data or their leading principal components and then the parameters of the underlying functions are estimated. A dissimilarity matrix is computed for all sampling sites, preferably using Gower's general similarity coefficient. Dissimilarities are then modified using the variogram to incorporate the form and extent of spatial variation. A nonhierarchical classification of sampling sites is performed on the leading latent vectors of the modified dissimilarity matrix by dynamic clustering to an optimum. The technique is illustrated with results of its application to soil survey data from two small areas in Britain and from a transect. In the case of the latter results of spatially weighted classifications are compared with those of strict segmentation. An appendix lists a Genstat program for a spatially constrained classification using a spherical variogram as an example.