一种知识约束下的多目标土壤空间抽样优化模型

土壤空间抽样优化需要综合考虑抽样精度、成本、代表性以及样点数量与空间布局等多目标,属于典型的NP-Hard空间优化决策问题。先验知识的应用以及多目标的博弈能够有效地提高抽样精度和效率。通过研究土壤空间抽样先验知识及其空间分层技术,以及土壤空间抽样方案与粒子群算法映射关系,建立了基于知识约束下多目标粒子群算法的土壤空间抽样优化模型。模型以最小克里金方差和最大熵为抽样目标,以分层最小样本量、空间阻隔和可达性为约束条件,结合目标规划法进行多目标帕累托优化方案求解,并以陕西省横山县为实验区验证了模型的有效性。实验结果表明,该模型相比传统方法具有较高的收敛效率和抽样精度,先验知识与目标规划法的应用显著提升了抽样方案代表性,能够为土壤空间抽样以及土壤质量监测网络构建提供新的技术支撑。     

基于点云卷积的点云分类方法研究

基于深度学习方法,借鉴二维图像卷积的思想,设计了一种适合三维点云的卷积操作。点云卷积的作用域是局部球形邻域,输入为三维坐标和空间几何关系。通过点云卷积提取局部特征,使用最远点采样算法采集邻域中心点,根据半径构建球形局部邻域,使用多层感知器（multi-layer perceptron,MLP）网络学习空间关系权重,将学习到的关系权重和输入特征相乘,实现卷积操作。基于三维点云卷积,构建了一个多层分类网络模型实现点云分类。使用道路场景的黄石路数据集进行分类实验,结果证明了所提方法的有效性。     

V-代约束的结构保持空间插值算法

针对现有空间插值方法对样点空间分布及结构约束考虑较少,难以保真原有空间数据的统计参量等问题,利用Voronoi和Delaunay的相互关系,建立了基于样点分布V-邻域结构的插值控制点自适应生成方法,构建了顾及样点分布结构与分布密度的结构保持空间插值方法。基于中国气象台站日均气温数据的方法验证与对比表明,相比于常用的空间插值算法,本文算法具有更好的结构自适应性,且对原始数据的空间统计特征具有更好的保持性。     

实景三维在土壤普查表层样点校核中的应用

针对传统的土壤表层样点校核方式耗时费力且质量不佳的问题，该文提出了基于实景三维实现土壤表层样点快速校核的方法。在实景三维中，采用缓冲区分析、叠置分析、网络分析等空间分析技术，实现了青岛市即墨区土壤表层样点的高效校核。研究结果表明：实景三维能够较好地解决传统校核方式中现场踏勘局限性多、资料利用不足、环境变量把控不强、校核成本高等弊端，并且当表层样点数量越多，地形地貌越复杂时，优势越明显。该研究模式为第三次全国土壤普查提供了在实景三维中运用空间分析技术实现表层样点校核的借鉴，适宜在全国范围内推广。     

散乱点云数据精配准的粒子群优化算法

作为点云数据处理的关键步骤,点云数据配准的结果直接影响后续数据处理的精度。基于人工标靶和ICP思想的传统配准方法存在受环境影响、初始条件限制以及特征点提取困难等问题。针对传统地面激光扫描点云数据的高精度配准方法主要依赖人工标靶和特征点选取等局限,提出了一种改进的粒子群优化算法,以法向量叉积代数和最小作为适应度函数,对相邻点云重叠区域内的所有数据进行高效的全局搜索,在选取最佳配准点的基础上实现了散乱点云的精确配准。通过对多站扫描的高陡边坡岩体点云数据进行整体配准,并与ICP等经典算法进行对比实验,结果验证了本方法的可行性、有效性和稳定性,可以有效解决配准过程中标靶或同名特征点不易寻找的问题。     

利用粒子群算法优化的GPS高程拟合精度分析

为了获取研究区域内必要的基础数据,采用高精度的拟合模型进行GPS高程拟合的方法备受青睐。多面函数法适用于地形条件较复杂的研究区域,传统的多面函数拟合法很难达到预期效果。针对模型参数难以获取的问题,提出了基于粒子群算法优化的高程拟合方法,将粒子群优化算法分别与传统高程拟合法及蚁群算法改进的拟合结果比对分析。实验研究表明,采用粒子群算法优化的拟合结果优于传统拟合方法,模型精度提高了43.3%。在提高模型精度的同时,验证了粒子群算法获取特征点的收敛效果优于蚁群算法。充分证明了基于粒子群算法寻优过程的有效性,且验证了改进拟合方法的可行性,为高程拟合模型的研究进一步提供了参考价值。     

视觉认知的自适应多级岛屿群空间模式提取

针对当前电子地图显示范围以及人眼视觉分辨能力的限制,该文提出了符合视觉认知规律的自适应多级岛屿群空间模式,基于动态邻近图、最小生成树、最小面积外接矩形等概念设计了岛屿群多级空间模式提取算法。实验结果表明,该方法有效顾及了岛屿群显示的空间尺度,能够自适应地生成符合显示尺度要求的岛屿群多级空间模式,提高了空间模式识别的灵活性和有效性。     

利用贝叶斯抽样一致性的点云特征面拟合

针对传统随机抽样一致性算法在拟合特征面时对种子点的选择具有一定的随机性,造成循环次数过多、效率低下的问题,该文提出一种改进的随机抽样一致性算法——贝叶斯抽样一致性算法。首先建立柱面、球面、圆环面、平面的数学模型;然后用贝叶斯抽样一致性算法提高抗噪性,并用二维直方图统计方法对贝叶斯抽样一致性算法中的局内点先验概率估计进行改进;最后,对局内点用非线性最小二乘进行拟合。将该方法与基于随机抽样一致性算法的特征面拟合方法进行了对比和分析,实验结果证明,贝叶斯抽样一致性算法能够更高效地实现局部特征面拟合。     

移位安全区约束下的建筑物群移位免疫遗传算法

对于采用启发式或群智能搜索的组合最优化移位算法,地图要素空间关系与空间分布特征的保持是一个难题.本文基于免疫遗传算法提出一种移位安全区约束下的建筑物群最优化移位方法.该方法将建筑物群的移位问题定义为一个多目标最优化问题,然后采用免疫遗传算法搜索最优解.为了尽量保持建筑物群的空间关系和总体分布特征,避免出现拓扑错误,采用Voronoi图和缓冲区构建每个建筑物的移位安全区,以限定建筑物的移位范围;同时,采用建筑物群组整体移位策略,保持局部空间分布模式.最后,以北京市某部分街区建筑物群的移位为例验证改进算法的有效性,结果表明所实现算法能够在解决邻近冲突的同时,较好地保持地图目标间的空间关系和空间分布特征.     

基于多源信息与深度学习特征提取的人口空间抽样方法

人口抽样调查是通过人口样本估算区域人口总体的一种手段。由于人口分布通常具有空间差异性,传统的抽样调查理论难以满足日益增长的空间抽样需求,合理高效的人口空间抽样调查方法对于人口统计、研究人类活动、解决城市问题等有重要意义。本文提出一种基于多源信息与深度学习特征提取的人口空间抽样方法。在不透水面信息的辅助下,利用四叉树分割进行分层抽样,初步选择出可能存在人口分布的调查样本,并通过深度学习的常用模型——卷积神经网络估算样本建筑物密度,以辅助最终调查样本的选择与调查方案的制定。研究结果证明,该方法能够有效地筛选与人口分布密切相关的抽样区域,排除大量的无用样本,提高了人口调查的效率,节约了大量调查成本。     

Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling

ABSTRACT

To analyze the efficiency of area estimations (i.e. estimation accuracy and variation of estimation) impacted by crop mapping error, we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology. The results show that the estimation efficiency is influenced by the combination of the sample size and the error level. Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit (i.e. higher estimation efficiency). Further, sampling performance differed based on the heterogeneity of the crop area. The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones. Therefore, to obtain higher estimation efficiency, a larger sample size and lower error level or both are needed, especially in heterogeneous areas. We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area. The appropriate sample size for these areas then can be determined according to all three factors: heterogeneity, expected estimation efficiency, and sampling budget. Overall, extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.     

点集数据不规则形状时空异常聚类模式挖掘研究

传统扫描统计方法在进行时空异常聚类模式挖掘时,受扫描窗口形状的限制,不能准确地获取聚类区域形状。提出一种改进的不规则形状时空异常聚类模式挖掘方法stAntScan。新方法基于26方位时空邻近单元格构建时空邻接矩阵,再对蚁群最优化扫描统计方法进行改进,使其能适应三维大数据量的时空区域扫描。模拟数据和真实微博签到数据的实验证明,stAntScan能有效地识别时空范围内的不规则形状异常聚类,并且准确性较经典的SaTScan方法高。     

土壤重金属污染灰色综合评价模型

针对稀疏采样难以准确估测区域土壤重金属综合污染情况和迁移变化规律的问题,提出基于GIS的多属性决策组合赋权灰色综合评价模型。首先采用GIS技术揭示土壤重金属空间变异和污染分布格局;然后利用最大化熵理论集成主客观因素,架构优化组合赋权的土壤重金属污染灰色综合评价体系;最后以试验区土壤中8种(铜、锌、铅、镉、砷、铬、汞、镍)重金属的综合污染情况为例,检验该方法应用效果。结果表明:最优组合权重的灰色综合分析方法兼顾主观偏好和客观属性,其评价结果具有更高的可信度和风险辨识度,提高了综合评价的合理性与有效性,可为土壤重金属污染监测提供方案参考。     

Spatial Assessment of Soil Organic Carbon Density Through Random Forests Based Imputation

Regional estimates of soil carbon pool have been made using various approaches that combine soil maps with sample databases. The point soil organic carbon (SOC) densities are spatialized employing approaches like regression, spatial interpolation, polygon based summation, etc. The present work investigates a data mining based spatial imputation for spatial assessment of soil organic carbon density. The study area covers Andhra Pradesh and Karnataka states of India. Field sampling was done using stratified random sampling method with land cover/use, soil type, agro-ecological regions for defining strata. The spatial data at 1 km resolution on climate, NDVI, land cover, soil type, topography was used as input for modeling the top 30 cm Soil Organic Carbon (SOC) density. To model the SOC density, a Random Forest (RF) based model with optimal parameters and input variables has been adopted. Experiment results indicate that 500 number of trees with 5 variables at each split could explain the maximum variability of soil organic carbon density of the study area. Out of various input variables used to model SOC density, land use / cover was found to be the most significant factor that influences SOC density with a distinct importance score of 34.7 followed by NDVI with a score of 12.9. The predicted mean SOC densities range between 2.22 and 13.2 Kg m^?2 and the estimated pool size of SOC in top 30 cm depth is 923 Tg for Andhra Pradesh and 1,029 Tg for Karnataka. The predicted SOC densities using this model were in good agreement with the measured observations (R?=?0.86).     

利用海鸥理论的路径优化算法分析

针对GIS空间分析需要经常解决的路径优化问题,本文研究了一种新型的群体智能空间路径优化算法,即海鸥优化算法（SOA）。通过重新定义海鸥位置的表示方式和更新策略,将海鸥优化算法从连续域转换到离散域,建立离散海鸥优化算法（DSOA）,同时引入随机异变因子,使海鸥有能力跳出局部最优值。为了验证DSOA的可靠性,通过定义适应度函数和可行解空间,实现利用离散海鸥优化算法求解经典的旅行商最短路径问题。试验结果表明,DSOA在解决最优路径问题上具有良好的稳健性,在空间分析方面具有较强应用潜力。     

不规则三角网数字水深模型缓冲面快速构建的滚动球加速优化算法

针对TIN_DDM缓冲面构建与应用中存在的数据类型特殊、算法效率与模型精度不匹配的问题,本文将滚动球模型应用扩展至TIN_DDM缓冲面的构建过程。在分析滚动球模型构建精度局限的基础上,建立了滚动球半径关联的滚动球模型整体精度控制方法;结合大数据量TIN_DDM缓冲面多次构建的应用效率需求,阐明了关键采样点与滚动球半径对TIN_DDM缓冲面构建效率的影响规律;设计了TIN_DDM缓冲面构建关键采样点的判定准则,建立了关键采样点与滚动球半径的数值关联关系;提出了一种基于滚动球加速优化模型的TIN_DDM缓冲面快速构建算法,算法时间复杂度为O(n)。试验结果表明:本文算法可实现任意缓冲半径条件下TIN_DDM缓冲面的多次快速构建,且算法精度控制在2σ内。     

The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs

Four binary thematic maps with combinations of two spatial autocorrelation levels and two different class proportions are simulated to study their effect on the precision of accuracy measures from different sampling designs. A series of eleven sample sizes (from a minimum of 25 to a maximum of 1296) are simulated using three popular sampling designs, including simple random sampling (SRS), systematic sampling (SYS), and stratified random sampling (StrRS) on the four simulated maps. The conventional error matrix and related accuracy measures are calculated for each simulation, and the precision of different estimates of accuracy measures is compared among the three sampling designs.The selection of a particular sampling design and sample size depends on the spatial autocorrelation level, the class proportion difference, and the accuracy indices that a given application requires. In general, the class proportion difference has a greater impact on the performance of different sampling methods than the spatial autocorrelation level does on a map. For estimating the accuracy of individual classes, stratified sampling achieves better precision than SRS and SYS with smaller sample sizes, especially for estimating the small class. For estimating the overall accuracy, different sampling designs achieve very similar levels of precision with fewer samples. To achieve a better estimate of the kappa coefficient, stratified random sampling is recommended for use on a map with a high class proportion difference, while random sampling is preferred for a map with low spatial autocorrelation and a low class proportion difference.     

GIS典型几何计算算法的并行化及优化研究

空间数据规模的快速增长对传统矢量数据分析方法提出了更高的计算效率和处理规模要求。随着计算机硬件和软件技术的进步,并行计算为提高GIS中典型几何计算算法的计算效率、扩大问题处理规模提供了有效手段。本文在Visual Studio 2010中,使用标准C++编程语言,基于GDAL(Geospatial Data Abstraction Library)库实现空间数据的读写操作,针对线简化算法的并行化问题,在高性能计算环境下对并行任务调度策略、并行计算粒度、数据分解方法等多个核心内容开展研究。在完成相关串行算法的基础上,实现了该算法的并行化和优化设计,为相关的矢量数据空间分析方法的多核并行优化提供了思路和参考。     

Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm

The objective of this paper is to demonstrate a new method to map the distributions of C₃ and C₄ grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C₃ and C₄ grasses in our study area such as elevation, soil pH, soil organic matter and soil texture.