面向自动推荐的表示方法规则的形式化表达研究

在利用专题制图软件制作统计专题地图时,普通用户由于没有表示方法知识,无法选择合适的表示方法表达专题要素。为了解决这一问题,本文将表示方法规则嵌入到制图软件中,深入研究了面向自动推荐的表示方法规则的形式化表达策略。先后介绍了表示方法规则的特征、表示方法简单规则和组合规则的形式化表达策略、表示方法规则的运行机制,最后进行了可用性试验,说明该形式化表达策略具有一定的理论和应用价值。     

空间数据对比规则库研究——以道路数据为例

随着GIS技术的发展,空间数据已被应用于各个领域,对空间数据的对比分析也提出了更高要求。本文提出了一种对比规则库的设计,可定义的空间、属性数据对比规则。通过对比规则库,可大大增强GIS系统数据对比分析功能的可扩展性、灵活性,提高操作人员的工作效率。本文还讨论了某省公路局历史数据对比分析系统的具体实现方案。     

基于非下采样Contourlet变换的极化SAR图像融合与去噪

针对非下采样Contourlet变换(NSCT)在处理噪声影像中具有的优势,以及同极化SAR图像(HH、VV)之间的相关性与互补性,本文实验了一种基于非下采样Contourlet变换的极化图像融合方法。该方法首先对每个极化图像进行多尺度、多方向分解,然后对不同分解子带系数分别采用有利于斑点噪声去除和信息增强的融合规则进行融合,最终通过NSCT反变换得到融合图像。通过信息熵、相关系数以及等效视数等指标的评价,验证了该方法可以有效地实现信息增强,同时该方法也在一定程度上降低了斑点噪声的负面影响。     

基于金字塔模型的规则格网数据可视化交互编辑

分析了规则格网数据分块的原则和方法,提出了规则格网数据金字塔模型的构建方法;系统论述了基于金字塔模型的规则格网数据可视化编辑原理、方法和实现过程,特别对格网数据编辑结果向金字塔模型中其他数据层的传播进行了全面研究,编程实践了单点和区域两种方式对基于金字塔模型的规则格网数据的可视化交互编辑。     

利用无人机和面向对象技术快速提取田坎面积

快速、准确、低成本的获取田坎面积,对于土地整治区整治计划的安排和耕地保护具有重要的意义.本研究首先利用剖面曲率、坡度与田坎之间的分布关系,对影像进行分幅处理,对于地物信息不明显的石堰,对比其与主要区分对象耕地在可见光波段上反射率的不同,运用波段运算,增强其地物信息.其次结合基于规则的面向对象分类方法,构建田坎的分类规则...     

知识与规则驱动的国土资源空间数据整合方法研究

本文根据GIS领域知识与规则研究的现状和趋势,结合国土资源行业空间数据的现状和要求,研究了国土资源空间数据知识与规则的研究方法与应用模式,提出了通过提取国土资源空间数据知识与规则,利用语义和组件实现规则的驱动,解决异构国土资源空间数据转换和同构空间数据检查入库自动化难题的国土资源空间数据整合方法。然后以面向地籍、土地利用现状的专题空间数据和面向地籍管理的基础空间数据的知识与规则提取、语义及规则驱动的语义和组件驱动的实现为实例进行了阐述。     

基于可操纵金字塔和IHS变换的遥感影像融合

提出了一种基于可操纵金字塔和IHS变换的遥感影像融合方法。对多光谱（MS）图像的强度分量和全色图像（PAN）分别进行可操纵金字塔分解,针对金字塔分解后的低频、高频和各带通子图像分别采取不同的融合规则获得融合子图像,再先后通过可操纵金字塔框架下的逆变换和IHS逆变换得到融合图像。实验结果表明,该方法在增强影像细节信息、保...     

基于金字塔模型的规则格网数据可视化交互编辑

分析了规则格网数据分块的原则和方法,提出了规则格网数据金字塔模型的构建方法;系统论述了基于金字塔模型的规则格网数据可视化编辑原理、方法和实现过程,特别对格网数据编辑结果向金字塔模型中其他数据层的传播进行了全面研究,编程实践了单点和区域两种方式对基于金字塔模型的规则格网数据的可视化交互编辑.     

基于改进粗糙集和NSCT的红外遥感图像增强

提出了一种基于改进粗糙集和NSCT的红外遥感图像增强方法。该方法首先利用NSCT对图像进行分解,得到多层多方向子带系数;然后根据相邻尺度和不同方向的子带中图像噪声、脆弱边缘等不同成分的系数分布,使用粗糙集对其进行分块,并制定合理的决策规则;再通过集合运算对系数中不同子块采用不同的处理方法,一方面抑制噪声,另一方面保护图像中的脆弱边缘结构,并采用增强函数对其进行不同程度的增强;最后对处理过的NSCT系数进行重构,获得增强后的红外图像。实验表明,该算法相对于其他传统红外遥感图像增强算法具有较好效果。     

空间实体关联规则提取技术研究

总结了空间关联规则的定义及表达方法,针对传统关联规则提取方法的不足,根据提取的关联性指标不同,提出了基于空间拓扑关系提取法、基于空间距离、基于空间方向关系提取法和基于属性相似性提取法四种空间关联规则提取方法.设计了空间关联规则提取的技术流程,通过实例验证了方法的有效性,最后探讨了今后研究的方向.     

测绘行业涉密计算机安全审计技术研究

根据测绘行业数据成果安全保密管理的实际需求,设计了一种基于规则库的涉密计算机安全审计系统。本文分析了审计系统的组成,各组成部分的构造方法以及相互关系,给出了该系统的集成和管理方法,特别是结合实例讨论了规则库的设计。     

专题地图制图决策系统规则库的设计

在分析已有的专题地图制图决策支持系统的有关理论的基础上,总结相关制图知识,提出规则库设计的方法并设计部分规则实例,最后对规则库如何映射到关系数据库做了一些探讨,并建立了模型。     

交互式基础地理数据制图综合方法的研究

本文对基于地理数据库的制图综合的自动化实现方法作了研究和实践,以期解决基于基础地理数据库的制图综合的生产问题。本文对制图综合的自动化方法实现目前存在的难点作了必要的论述,结合测绘生产单位的实际,提出了基于规则的交互式制图综合方法,并形成了一套较为完整的基于基础地理数据库的制图综合生产工艺流程。     

高分辨率多光谱影像城区建筑物提取研究

城区高空间分辨率遥感数据由于存在大量同物异谱和异物同谱现象,应用传统的基于像元光谱分类的方法进行建筑物分类提取难以取得满意的效果。本文发展了一种从高分辨率Ikonos卫星影像上基于知识规则的面向对象分类提取城区建筑物方法,包括如下步骤：(1)融合1m全色和4m多光谱波段影像,生成1m分辨率的多光谱融合影像;（2）分割融合影像;（3）执行基于对象光谱的最近邻监督分类;（4）应用模糊逻辑分类器结合光谱、空间、纹理和上下文特征等知识规则进行建筑物分类。精度统计结果表明,本文提出的分类方法提取城区建筑物取得了93%的精度。     

Integration of high-resolution imagery and LiDAR data for object-based classification of urban area

This paper investigates the synergistic use of high-resolution multispectral imagery and Light Detection and Ranging (LiDAR) data for object-based classification of urban area. The main contribution of this paper is the development of a semi-automated object-based and rule-based classification method. In the implemented approach, the diverse knowledge about land use/land cover classes are transformed into a set of specialized rules. Further, this paper explores supervised Gaussian Mixture Models for classification, which have been primarily used for unsupervised classification. The work is carried out on test data from two different sites. Contribution of the LiDAR data resulted in a significant improvement of overall Kappa. Accuracy assessment carried out for aforementioned classification methods shows higher overall kappa for both the study sites.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

Comparison of upscaling cropland and non-cropland map using uncertainty weighted majority rule-based and the majority rule-based aggregation methods

Aggregation method is seriously impacted by the landscape characteristics, which has been emphasized due to proportional errors. This research proposed an uncertainty weighted majority rule-based aggregation method (UWMRB) to upscale the cropland/non-cropland map. The Cropland Data Layer for 2016 at 30m resolution, with its corresponding confidence level data, were collected to conduct the experiment using UWMRB and majority rule-based aggregation method. Proportional errors of crop/non-crop were used to assess the accuracy of the two methods. Ordinal logistic regression was used to obtain the probability of an error occurring to predict the uncertainty of both methods. The results show that UWMRB can achieve the lower proportional errors with lower uncertainty. Also, it can reduce the influence of complexity and fragmentation of landscape on aggregation performance. Additionally, the examination of UWMRB provides an important view of application of uncertainty information for upscaling land cover maps in an efficient way.     

专家系统在 TM 图像分类中的应用

提高TM图像的分类精度,是图像处理及应用领域中一个很重要的研究课题。本文在总结已有成果基础上,首先利用现有的统计分类技术,对待分类图像进行预分类,并检测出“不确定”像元。然后综合光谱、地理、土壤类型、早期判别结果、目视判读经验等各种知识和信息,充分发挥专家系统的推理判断能力,对“不确定”像元的类别作进一步判别,使得整幅图像的分类精度得到改善。并据此初步建立了一个土地利用的分类系统。试验证明,这种分类方法的精度比仅用单一多光谱信息的统计分类法(最大似然法)提高约8％。     

Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data

Vegetation phenology is a sensitive indicator that reflects the vegetation–atmosphere interactions and vegetation processes under global atmospheric changes. Fast-developing remote sensing technologies that monitor the land surface at high spatial and temporal resolutions have been widely used in vegetation phenology retrieval and analysis at a large scale. While researchers have developed many phenology retrieving methods based on remote sensing data, the relationships and differences among the phenology retrieving methods are unclear, and there is a lack of evaluation and comparison with the field phenology recoding data. In this study, we evaluated and compared eight phenology retrieving methods using Moderate Resolution Imaging Spectroradiometer (MODIS) and the USA National Phenology Network data from across North America. The studied phenology retrieving methods included six commonly used rule-based methods (i.e., amplitude threshold, the first-order derivative, the second-order derivative, the third-order derivative, the relative change curvature, and the curvature change rate) and two newly developed machine learning methods (i.e., neural network and random forest). At the large scale, the start of the season (SOS) values, derived by all methods, had similar spatial distributions; however, the retrieved values had large uncertainties in each pixel, and the end of the season (EOS) inverted values were largely different among methods. At the site scale, the SOS and EOS values extracted by the rule-based methods all had significant positive correlations with the field phenology observations. Among the rule-based methods, the amplitude threshold method performed the best. The machine learning methods outperformed the rule-based methods in terms of retrieving the SOS when assessed using the field observations. Our study highlighted that there were large differences among the methods in retrieving the vegetation phenology from satellite data and that researchers must be cautious in selecting an appropriate method for analyzing the satellite-retrieved phenology. Our results also demonstrated the importance of field phenology observations and the usefulness of the machine learning methods in understanding the satellite-based land surface phenology. These findings provide a valuable reference for the future development of global and regional phenology products.     

Identification of drainage patterns using a graph convolutional neural network

Various geological factors shape drainage patterns. Identifying drainage patterns is a classic problem in topographical knowledge mining and map generalization. Existing rule-based methods rely heavily on the parameter settings of cartographers for drainage-pattern recognition. These methods effectively identify drainage patterns in specific areas but require manual parameter tuning to identify drainage patterns in other areas. Owing to the complexity of topological and geometric characteristics, drainage pattern recognition involves nonlinear problems, and it is difficult to build mapping relationships between characteristics and patterns using rule-based methods. Therefore, we proposed a data-driven method based on a graph convolutional neural network to avoid heavy reliance on human experience and automatically mine implicit relationships between characteristics and drainage patterns. First, six typical drainage patterns (dendritic, rectangular, parallel, trellis, reticulate, and fanned) were listed based on map specifications, and the unique characteristics of each drainage pattern were illustrated. Subsequently, the drainage graphs were constructed. The characteristics of the whole, local, and individual units in the drainage networks were quantified based on drainage vector data. Finally, an identification model was developed using graph convolution, self-attention pooling, and multiple fully connected layers for drainage pattern recognition. After training and testing, the accuracy of our model (0.801 ± 0.014) was better than that of the rule-based method (0.572 ± 0.000) and the traditional machine learning methods (less than 0.733 ± 0.016). The results demonstrate that the ability of our model to identify drainage patterns surpasses that of other methods.