空间信息应用集成与互操作

基于Web Services技术和GML(geography markup language)技术,给出了一个新的空间信息应用集成与互操作系统--π系统框架(PKU applications integration systemframework),将不同的空间信息获取服务和其他空间信息查询分析服务有机地集成起来,屏蔽了空间信息应用的分布性和GIS平台的异构性.对于π系统框架的使用者来说,如同在网络中使用同一GIS平台开发一样,能够使用不同GIS厂商平台的空间分析服务,也能使用不同空间信息库进行空间信息的存储和查询.     

融入空间信息的湿地信息提取技术

以安徽省湿地为对象,采用TM影像,在去噪声处理和主成分分析的基础上,以一种基于光谱和空间信息相结合的分类方法,对研究区的湿地信息进行了提取。结果表明:以主成分分析法对数据进行处理发现,前4个主成分的累积贡献率为99.5%,可代表原始数据的绝大部分信息; 结合空间信息的提取方法对各类型湿地的平均提取精度可达84.6%,有效地改善了“麻点”现象,而采用光谱信息的平均提取精度仅为69.3%。     

Selectivity estimation using compressed spatial information

Spatial selectivity estimation is one of the essential studies to get query responses rapidly and accurately with the limitation of memory space. Currently, there exist several spatial selectivity estimation techniques such as random sampling, histogram, and parametric. Especially, Cumulative Density Histogram guarantees accurate estimation for rectangle object which has multiple-count problem. However, it requires large memory space because of retaining four sub-histograms for spatial data. Therefore in this paper, we propose a new technique Cumulative Density Wavelet Histogram, called CDWH, which is the combination of Cumulative Density Histogram and Haar Wavelet Transform, a compressed technique. The proposed method simultaneously takes full advantage of their strong points, high accuracy provided by the former and economization of memory space supported by the latter. Consequently, our technique is able to support estimates with relatively low error and retain similar estimates even if memory space is small.     

Selecivity estimation using compressed spatial information

Spatial selectivity estimation is one of the essential studies to get query responses rapidly and accurately with the limitation of memory space. Currently, there exist several spatial selectivity estimation techniques such as random sampling, histogram, and parametric. Especially，Cumulative Density Histogram guarantees accurate estimation for rectangle object which has multiple-count problem. However，it requires large memory space because of retaining four sub-histograms for spatial data. Therefore in this paper， we propose a new technique Cumulative Density Wavelet Histogram，called CDWH，which is the combination of Cumulative Density Histogram and Haar Wavelet Transform，a compressed technique. The proposed method simultaneously takes full advantage of their strong points，high accuracy provided by the former and economization of memory space supported by the latter. Consequently，our technique is able to support estimates with relatively low error and retain similar estimates even if memory space is small.     

Selectivity estimation using compressed spatial information

Spatial selectivity estimation is one of the essential studies to get query responses rapidly and accurately with the limitation of memory space. Currently, there exist several spatial selectivity estimation techniques such as random sampling, histogram, and parametric. Especially, Cumulative Density Histogram guarantees accurate estimation for rectangle object which has multiple-count problem. However, it requires large memory space because of retaining four sub-histograms for spatial data. Therefore in this paper,we propose a new technique Cumulative Density Wavelet Histogram, called CDWH, which is the combination of Cumulative Density Histogram and Haar Wavelet Transform, a compressed technique. The proposed method simultaneously takes full advantage of their strong points, high accuracy provided by the former and economization of memory space supported by the latter. Consequently, our technique is able to support estimates with relatively low error and retain similar estimates even if memory space is small.     

First emergency spatial information centre establishes in Beijing

Chinese scientists and German scientists conducted site selection for the project ＂Surface Observation of Radiation and Turbulence Flux＂ on Qomolangma from July 3 to July 4, 2009.     

基于SOA的电子政务空间地理信息服务与实现

为了满足电子政务对空间信息服务的需求,并解决现存GIS软件的一些局限性,本文尝试把SOA技术引入到GIS领域,提出基于SOA的电子政务地理信息服务概念.设计了支持软件功能松偶合的基于SOA电子政务地理信息系统架构,采用再工程技术对现存GIS系统进行了改造.最后,介绍了基于SOA的电子政务地理信息服务实验情况,对提出的方案进行了验证.     

空间信息网络中的资源映射与调度研究

目前空间信息网络中卫星资源不尽相同,且卫星节点的星间链路动态变化,给资源映射造成困难,使得网络资源利用不充分.如何实现空间资源利用的最大化,成为亟待解决的难题.为解决上述问题,提供了一种方法:首先,将卫星网络可以提供的资源和任务指标映射表达为多维矢量;再通过线性规划,使资源矢量和任务矢量相匹配,从而使网络资源能更充分地被利用.     

云环境下的流式空间信息服务

随着物联网、传感器技术与应用的发展,对大规模多维空间数据集成与服务的需求愈加迫切.如何实现基于大规模、多维空间数据流的实时计算成为空间数据处理领域的难点.结合云计算的特点,提出针对高速、大流量空间数据的实时处理方法.通过对多维、异构空间数据进行智能处理,生成结构化、简洁化的中间属性集;利用针对高速数据流的大规模数据实时处理方法,解决Map/Reduce难以满足此类计算实时性要求的不足.在此基础上,设计了流式空间信息组织模型与云端适配方法,对方法中的关键技术问题进行了描述.实践表明,该方法可显著提高动态空间信息的服务质量与运行性能.     

淮河流域降雨时空变异与信息熵分析

基于淮河流域蚌埠闸以上173个雨量站1956—1997年共41年的日降雨资料,采用Hurst系数法和信息熵方法分析了淮河—沙颍河流域降雨时空变化规律。结果表明:淮河干流与沙颍河中上游大部分地区的Hurst系数都小于0.5,而涡河流域则大于0.5,淮河—沙颍河流域与涡河流域降雨存在一定的地区差异性;在流域南部,各站的年降水在时间上具有比较强的随机性,降水量的大小也比较接近,该规律在流域北部不明显;全流域与1号、2号和3号子区域的空间熵的变化有较高的相似性,与4号、5号和6号子区域的相似性则比较小,主要原因是4号~6号子区域同属涡河流域,与淮河—沙颍河流域存在一定的差异性,这与基于Hurst系数方法得到的结果类似。     

上海住房市场信息不完全度的时间与空间变化规律

为定量计算住房市场的信息不完全度,并揭示住房市场信息不完全度的时间与空间变动规律,首先构建计算住房市场信息不完全度的公式和模型;其次利用上海2000—2007年住房市场交易数据,建立上海住房市场H edon ic价格模型,并计算了上海住房市场信息不完全度及其时间与空间分布。研究表明:上海住房市场的信息不完全度随时间表现出先逐年快速上升再逐年缓慢下降的趋势,2004年到达最高点;上海中心城区的住房市场信息不完全程度远高于周围城区,而各非中心城区之间差异不大。     

结合色彩和空域信息的一种CBIR方法

提出了结合色彩和空域信息的CBIR方法.该算法采用色彩和空域信息相结合的办法来提取图像的特征向量,生成色彩-空域共生矩阵.实验结果表明,该方法能有效地提高检索的正确性.     

空间数据库更新过程中增量信息产生原因分析

为了能够实现空间数据新旧版本之间由于时空变化产生的增量信息的快速提取和分发,作者以小比例尺地形数据库中居民地要素为例分析了增量信息产生的原因.提出了基于时空变化类型和数据差的增量信息概念,变化类型描述增量信息的语义,数据差表达增量信息的内容.在此概念的基础上详细地分析了居民地可能发生的各种变化类型及其产生的数据差表达方式.首先将变化分为主观变化和客观变化.主观变化又详细分为3类,客观变化详细分为12类,并且将各种客观变化类型的增量信息的描述形式化,便于计算机中编程实现增量信息的自动化提取.该方法已经成功的应用于中国1∶250 000地形数据库更新信息发布应用系统中.     

数字农业空间信息管理平台的设计与实现

根据数字农业空间信息管理平台的建设目标和要求,进行了空间数据库和属性数据库的设计与建设.在此基础上,采用SQL Server2000、ArcSDE、VB6.0和ArcEngine四者结合进行了平台总体架构和功能的设计,并进行了平台的开发,实现了平台与数据管理、地图基本操作、农业信息综合查询、农业空间分析、土地斑块调整、制图与输出系统等功能.     

空间信息尺度变换在铁路数据处理中的应用

铁路地理信息系统是一个具有等级结构的复杂系统,包含了各种尺度的空间信息,这就需要在实际应用中进行空间信息的多尺度变换。本文通过分析铁路空间信息的多尺度特征,结合信息自动综合方法,给出了铁路空间信息多尺度变换的方法。     

Recovery of spatial learning deficits after decay of electrically induced synaptic enhancement in the hippocampus

A widespread interest in a long-lasting form of synaptic enhancement in hippocampal circuits has arisen largely because it might reflect the activation of physiological mechanisms that underlie rapid associative learning. As its induction normally requires the 'Hebbian' association of activity on a number of input fibres, we refer to the process as long-term enhancement (LTE) rather than long-term potentiation (LTP), to emphasize its distinction from the ubiquitous, non-associative 'potentiation' phenomena that occur at most synapses, including those exhibiting LTE. Among other evidence that LTE might actually have a role in associative memory is the demonstration that repeated high-frequency stimulation, which saturated the inducible LTE, caused a severe deficit in spatial learning, although it had no effect on well established spatial memory. These results were consistent with a widespread view that information need only temporarily be stored in the hippocampal formation in order for long-term memories to be established in neocortical circuits. In this context, it is important to understand whether the possible underlying synaptic changes are of a permanent character, or are relatively transient. A second question is whether the actual cause of the observed learning deficit is the distruption of the synaptic weight distribution, and/or the limitation of further synaptic change, which presumably results from experimental saturation of the LTE mechanism. Alternatively, the deficit could be a consequence of some unobserved secondary effect of the high-frequency electrical stimulation. Here we demonstrate that learning capacity recovers in about the same time that it takes LTE to decay, which strongly favours the first possibility and supports the idea that LTE-like processes actually underlie associative memory.     

Effect of spatial distance on search information correlation in complex systems

Spatial distance has a remarkable effect on the attended mode of a network embedded in a certain space. First, we investigate how spatial restriction leads to information-information correlation that is strong, linear and positive in real networks. We then construct a two-dimensional space, define the action radius R for nodes of networks, and propose a class of models that depend on spatial distance. Information correlation of the models is consistent with that of real networks. The spatial distance plays ...     

基于空间和光谱信息保持的多光谱图像融合算法

文章采用基于空间和光谱信息保持的多光谱图像融合框架,以生成具有高空间质量的多光谱图像。本文采用的融合框架的能量泛函包括四项,第一项为边缘自适应提取约束项,从全色图像中自适应提取边缘细节信息并注入多光谱图像中;第二项为线性组合系数约束项,基于对多光谱图像各波段线性组合系数的估计以改善融合图像的空间质量;第三项为光谱信息保持约束项,基于L2范数分波段估计模糊核以保持多光谱图像的光谱信息;第四项为波段比例关系保持约束项,基于融合前后波段之间的比例关系相等的假设以减轻融合图像光谱失真。在QuickBird和Pavia University图像数据上进行仿真实验,结果表明,与SFIM、MTF_GLP、MTF_GLP_HPM、PCA、GS、GSA、AIHS、GFPCA等算法相比,本文方法的融合图像具有较高的空间和光谱质量。     

Effect of spatial distance on search information correlation in complex systems

Spatial distance has a remarkable effect on the attended mode of a network embedded in a certain space. First, we investigate how spatial restriction leads to information-information correlation that is strong, linear and positive in real networks. We then construct a two-dimensional space, define the action radius R for nodes of networks, and propose a class of models that depend on spatial distance. Information correlation of the models is consistent with that of real networks. The spatial distance plays a leading role in generating assortative mixing by degree, while the generation of disassortative mixing relies on both the degree of preferential attachment and spatial restriction.