Integration of Imperfect Spatial Information

The theme of this paper is integration of information arising from observations of spatial entities and relationships. The assumption is that observations are imperfect; in particular, that they are imprecise and inaccurate. Each observation is made in a context that among other things provides a level of resolution. So, a treatment of integration of observations of this type must take account of multiresolution spatial data models. After an introduction, the paper discusses an ontology of imperfection, focusing on imprecision and inaccuracy. The paper goes on to consider logics that are appropriate for integration of information arising from imperfect observations. Two case studies, showing some of the facets of this treatment are developed in greater detail. The first case study considers integration of imperfect (inaccurate and imprecise) observations of a single spatial region. The second case study develops the theory of regions with broad boundary to address the issue of integrating imprecise observations of spatial relationships.     

A multi-resolution approach towards point-based multi-objective geospatial facility location

The placement of certain facilities, such as radars and wind turbines, requires careful planning according to very specific geographical and spatial requirements. Such placement problems are often solved by metaheuristics which find near-optimal solutions within a fraction of the time required to solve these problems exactly. The use of high-resolution representations of the feasible search space generally ensures a high level of solution quality and accuracy, but involves evaluation of a larger number of candidate solutions than lower resolution representations, and is therefore more time-consuming. A trade-off between solution quality and time requirements must therefore be achieved when choosing an appropriate resolution of data to include in geospatial facility location models. In this paper, we propose a novel explore-and-exploit, multi-resolution solution approach that takes advantage of the reduced computational requirements associated with lower resolution terrain data, while simultaneously benefitting from the quality of solutions returned at higher resolutions. Our multi-resolution approach is capable of outperforming analyses in which only highest resolution data are considered, both in terms of solution quality and solution time requirements.     

The enhancement of the cell-based GIS analyses with fuzzy processing capabilities

In order to store and process natural phenomena in Geographic Information Systems (GIS) it is necessary to model the real world to form computational representation. Since classical set theory is used in conventional GIS softwares to model uncertain real world, the natural variability in the environmental phenomena cannot be modeled appropriately. Because, pervasive imprecision of the real world is unavoidably reduced to artificially precise spatial entities when the conventional crisp logic is used for modeling.An alternative approach is the fuzzy set theory, which provides a formal framework to represent and reason with uncertain information. In addition, linguistic variable concept in a fuzzy logic system is useful for communicating concepts and knowledge with human beings. FuzzyCell is a system designed and implemented to enhance commercial GIS software, namely ArcMap^® with fuzzy set theory. FuzzyCell allows users to (a) incorporate human knowledge and experience in the form of linguistically defined variables into GIS-based spatial analyses, (b) handle imprecision in the decision-making processes, and (c) approximate complex ill-defined problems in decision-making processes and classification. It provides eight membership functions, inference methods, methods for rule aggregation, operators for set operations and methods for defuzzification.The operation of FuzzyCell is presented through case studies, which demonstrate its application for classification and decision-making processes. This paper shows how fuzzy logic approach may contribute to a better representation and reasoning with imprecise concepts, which are inherent characteristics of geographic data stored and processed in GIS.     

分布式仿真多分辨率建模方法及一致性维护

在分布式仿真中,同一实体的多个分辨率模型并发以及不同实体的各个分辫率模型之间的交互会导致数据表现不一致的问题。在分析现有多分辨率建模方法的基础上,提出了适应分布式仿真环境的不同分辨率模型的树状结构建模方法,其主要由聚合级实体模型和平台级实体模型组成。为解决树状结构建模方法中不同分辫率模型的数据表现不一致问题,将不同分辨率模型间的交互看成分布式数据库中的事务,由此提出了一个嵌套两段式提交协议算法来有效解决不同分辨率模型的一致性维护问题。     

基于数据库的多分辨率地形索引结构的研究

当空间应用进入到网络化后，人们迫切需要利用数据库的约束机制为空间数据提供更好的保护，目前数据库系统只能管理单一分辨率的地形空间数据。该文提出了一种可在关系数据库顶端实现的、支撑动态多分辨率地形与可视化数据调用的关系模型。该模型解决了在大规模地形可视化程序中，数据库系统客户端/服务器间的数据交换问题，利用了X树索引技术将多分辨率地形映射到关系模型中，利用特征空间将累进查询过程转换为几个简单的一维范围查询的联合，在各表中每个帧分别进行一次索引扫描，即可获得全部必要的数据，使其更好地处理大规模的地形数据。实验结果表明，利用该模型能够获得良好的检索效果。     

Dynamic Multi-Resolution Spatial Object Derivation for Mobile and WWW Applications

Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small.     

引入注意力机制的多分辨率人体姿态估计研究

针对人体姿态估计任务中多分辨率特征表征直接融合时存在无法有效利用特征图空间特征信息的问题,基于High-Resolution Net（HRNet）进行结构设计,构建出结合了通道域注意力和空间域注意力机制的多分辨率人体姿态估计网络GCT-Nonlocal Net（GNNet）,提出了一种基于注意力机制的多分辨率表征融合方法,在不同分辨率表征融合前由空间注意力提取出各分辨率表征更有用的空间特征信息来改进融合单元,使得各分辨率表征间的信息融合效果更佳,最终输出的高分辨率表征含有更丰富的特征信息,同时构造了Gateneck模块和Gateblock模块,其通过引入通道注意力更明确地对通道关系建模从而高效地提取通道信息。在MS COCOVAL 2017进行验证,结果显示提出的GNNet相较于SOTA级表现的HRNet在相当参数量与运算量的情况下获得了更高的准确度,mAP提高了1.4个百分点。实验结果表明,所提方法有效地提高了多分辨率特征表征融合效果。     

基于多分辨率实体的三维仿真系统研究

针对现代仿真系统对作战实体的多层次、高分辨粒度的应用需求,提出了基于多分辨率实体的战场三维仿真系统的总体结构,设计了系统的主要功能,给出了多分辨率实体三维建模与简化、实体模型库维护管理以及实体间分辨率匹配等关键技术途径与算法,并以坦克交战可视化仿真为例,分析了多分辨率实体建模及系统的集成方法,为基于多分辨实体的高精度、多层次战场三维仿真系统的研究提供了一种可行的技术途径。     

一种基于特征的遥感影像融合新方法

基于特征的影像融合主要是在突出目标地物的空间结构和纹理特征情况下的信息融合,在小波多分辨率分析理论基础上,采用小波变换方法对高分辨图像的目标地物边缘信息增强后与多光谱图像进行特征融合,在融合过程中,没有采用传统的HIS小波融合方法,而是采用RGB小波变换的方法,用多光谱图像中的R、G、B 3个波段经小波分解后的低频图像与特征增强后的高分辨率图像、经小波分解后的高频图像分别进行融合,再经RGB合成为彩色图像得到最后的融合图像,这样既改善了图像的清晰度和分辨率,同时也保留了原多光谱图像的光谱信息。最后通过实验验证了上述结论。     

一种基于特征的遥感影像融合新方法

基于特征的影像融合主要是在突出目标地物的空间结构和纹理特征情况下的信息融合。在小波多分辨率分析理论基础上，采用小波变换方法对高分辨图像的目标地物边缘信息增强后与多光谱图像进行特征融合。在融合过程中，没有采用传统的HIS小波融合方法，而是采用RGB小波变换的方法，用多光谱图像中的R、G、B3波段经小波分解后的低频图像与特征增强后的高分辨率图像经小波分解后的高频图像分别进行融合，再经RGB合成为彩色图像得到最后的融合图像，这样既改善了图像的清晰度和分辨率，同时也保留了原多光谱图像的光谱信息。最后通过实验验证了上述结论。