基于半变异函数的多极化SAR图像地表淹没程度分析

洪涝灾害范围的提取能够为灾害的动态监测、评估提取重要的数据。由于半变异函数能够反映图像数据的随机性和结构性,可以很好的体现地物的空间自相关性。而地物在SAR图像上也表现出很好的空间自相关性和纹理特征,各种淹没程度的地物在图像上也表现出不同的空间自相关性,所以将特定窗口下特定步长的半变异函数应用到地表淹没程度分析,证明该方法的可行性。     

结构化数据核函数综述

核函数是处理非线性问题的新方法,而对于字符串、图像和蛋白质等无法直接输入且具有一定结构信息的数据,则需用结构化数据核函数解决。基于此,对核函数进行初步探讨,描述了句法驱动核、模型驱动核和组合核3种基本的结构化数据核函数,对结构化核函数的应用进行分析归纳总结。     

语义支持的地理要素属性相似性计算模型

现势性是发挥数据应用价值的关键。鉴于在地理要素数据匹配过程中,常常因为分类标准不同或地图综合处理等原因而产生要素属性间的语义异质性,该文试图通过引入本体语义技术解决语义异质性问题,利用属性枚举的方法表达地理要素类别语义,构建地理要素分类本体结构;同时将地物属性信息划分为要素类别信息、地物名称信息以及辅助信息3个部分,提出了语义支持的地理要素属性相似性模型。该模型从概念语义内涵的角度计算地理要素类别的相似性;从地物名称字面出发计算地物名称相似度;根据不同的辅助属性类型计算辅助属性相似度。实验结果表明,同尺度多时态与跨尺度相同时间版本环境下,所提出的模型可有效合理地评价候选地物间的属性相似性,较好地为地理数据匹配提供支持。     

基于DOM和VBA的Word文档数据交互式抽取

针对信息系统Word文档中的数据提取问题,分析现有解决方案中存在的不足,提出交互式提取Word文档中半结构化数据的方法。该方法通过制定Word模板、编写宏函数并运用可交互的外部程序调用宏函数,将Word文档中指定的内容提取到数据库中。实现了Word文档中半结构化数据提取的方便性和灵活性。     

基于树编辑距离的工作流距离度量方法

在工作流的发现和聚类等应用中,需要对两个工作流模型的距离进行度量。因此,提出一种计算两个不同结构化工作流的距离定量度量方法。首先介绍了结构化工作流,并将每一个结构化工作流转换为流程结构树;然后基于两个结构树之间的树编辑距离来计算工作流之间的距离及相应相似度。该距离度量方法满足距离度量的3个属性,即同实体不可区分性、对称性和三角不等式性质。这些属性使得该距离度量方法可以在工作流模型管理活动中作为定量分析工具。实验结果表明,基于树编辑距离的工作流度量方法是可行的。同时,与基于邻接矩阵的距离度量方法相比,该方法考虑了不同结构之间的语义距离,有效验证了此方法的合理性。     

基于时空变异函数的Kriging插值及实现

Kriging（克里金）算法通常用于对空间变量进行插值,但不能直接应用于时空变量,它需要进行时空扩展。以月平均气温数据为例,运用时空Kriging方法结合R统计语言进行时空插值研究及其实现。通过时序分解去除气温数据中季节变化项,在分别得到空间变异函数和时间变异函数的基础上构建一类积和式时空变异函数来描述变量的时空相关结构,并给出基于R语言的具体实现步骤。将普通Kriging方法进行时空扩展,应用于气温数据的时空插值中。验证结果表明,基于时空变异函数的Kriging方法能提供较高精度的插值效果,这为时空变量的插值预测提供了有效的途径。     

大规模非结构化数据资源快速存储方法研究

非结构化数据资源具有较高的研究价值,伴随着信息化技术、互联网技术应用范围的扩大,非结构化数据资源规模随之增大,对其存储技术提出了较大的挑战,因此提出了大规模非结构化数据资源快速存储方法,采用层次聚类算法分组处理非结构化数据资源。以某一组非结构化数据资源为对象,结合数据资源传输距离、节点能量、传输方向等因素,确定非结构化数据资源转发路径,描述非结构化数据资源存储过程,制定分层扩展存储机制,从而实现大规模非结构化数据资源的快速存储。实验数据表明,在不同实验工况背景下,应用本文方法后获得的非结构化数据资源存储速率最大值为1 920 MB/s,非结构化数据资源存储位置准确性最大值为98%。     

沙化土地光谱特征初步分析

分析地物光谱特征与土地沙化间的数量关系,找出地物光谱特征变异的规律,从地物光谱的角度为遥感影像中沙化土地提取和定量分析提供依据.沙化土地光谱数据源分为单一地物类型级和复合地物类型级两种,野外测量时分别采取不同的采样方法.沙化土地光谱数据表现出随植被覆盖、植被类型、土壤、地类等的变化规律.     

基于模板知识的带噪音半结构文本数据自动分词方法

随机噪音会引起半结构化数据结构和语义的变化,从而造成半结构化数据提取或向结构化自动转换障碍,为此本文在分析噪声对半结构化文本数据带来的影响基础下,提出了一种基于语法模板结构知识的自动化分词处理思路和方法 ,可以提高带噪音的半结构化文本数据自动分词准确性,可为类似问题提供参考。     

一种蓝藻水华提取空间尺度效应分析

针对不同尺度遥感数据监测蓝藻水华必然会带来尺度效应的问题,以太湖为研究区,基于准同步的MODIS L1B产品数据以及HJ-1A/1B数据,分析两者获取水华信息的空间尺度差异,应用地统计学中的半变异函数对蓝藻水华分布的空间异质性进行研究,并进一步分析2种尺度下水华提取的空间尺度效应与空间异质性的关系。发现由NDVI计算方式非线性引起的差异可以忽略不计;在30m尺度上随机因素对空间异质性影响很低,样本区的空间异质性主要是由空间自相关性的降低引起的;2种尺度上水华提取差异与空间异质性的关系可以用清晰的线性关系表达。该研究对于下一步修正MODIS 250m尺度上的水华提取精度具有重要意义。     

航空影像辅助下的城区机载LiDAR汽车目标检测方法

机载激光雷达(LiDAR)技术的出现为地面汽车目标检测提供了新的途径。为了从机载LiDAR点云数据中提取汽车对象,根据不同地物的属性特征,提出了一种航空影像辅助下的城区机载LiDAR汽车目标检测方法。首先利用形态学开重建滤波完成地面和地物的分类,然后在地物点的基础上结合正射影像,通过归一化植被指数(NDVI)特征完成对植被和非植被地物的初步分类,最后在非植被地物的基础上,根据地物对象的形状特征及高程信息完成汽车和建筑物及阴影植被等非汽车对象的分类,从而完成汽车目标的提取工作。3个实验区的计算结果表明:该方法能有效从LiDAR点云中提取汽车目标,正确度和完整度的均值分别为95%和85%,满足实用性要求。     

Multi-scale Clustering Analysis on Chinese Urbans based on Nighttime Light Image

Understanding spatial distribution of urban clusters at regional and national scales is increasingly important for many fields especially urban planning.Previous Studies have demonstrated urban built\|up areas can be derived from stable nighttime light satellite (DMSP\|OLS) images.Population and economic variables (i.e.GDP) have been proved significant positive correlations with nocturnal light brightness.However,less studies focused on the spatial distribution of extracted urban built\|up area.an improved DBSCAN algorithm is proposed to cluster the urban objects extracted from nighttime light image in different scales based on density,of which our urban spatial clusters are proved corresponding with urban agglomerations identified by statistical data.The traditional DBSCAN method is based on points which is not the same case with urban objects.The inclusion relation is refined,assuming that only if all the vertexes of each polygon are within the given distance,it is included in the area.Moreover,the parameters for the DBSCAN clustering model are determined by valleys of distances of every objects to classify urban spatial clusters.Besides,in a larger scale,the clustering results imply the different patterns of urban agglomerations on both sides of the Huhuanyong Line.      

Object-based land-cover change detection applied to Brazilian seasonal savannahs using geostatistical features

A new method for remote-sensing land-use/land-cover (LULC) change detection is proposed to eliminate the effects of forest phenology on classification results. This method is insensitive to spectral changes caused by vegetation seasonality and uses an object-based approach to extract geostatistical features from bitemporal Landsat TM (Thematic Mapper) images. We first create image objects by multiresolution segmentation to extract geostatistical features (semivariogram parameters and indices) and spectral information (average values) from NDVI (normalized difference vegetation index), acquired in the wet and dry seasons, as input data to train a Support Vector Machine algorithm. We also used the image difference traditional change-detection method to validate the effectiveness of the proposed method. We used two classes: (1) LULC change class and (2) seasonal change class. Using the most geostatistical features, the change detection results are considerably improved compared with the spectral features and image differencing technique. The highest accuracy was achieved by the sill (σ² overall variability) semivariogram parameter (95%) and the AFM (area first lag–first maximum) semivariogram index (88.33%), which were not affected by vegetation seasonality. The results indicate that the geostatistical context makes possible the use of bitemporal NDVI images to address the challenge of accurately detecting LULC changes in Brazilian seasonal savannahs, disregarding changes caused by phenological differences, without using a dense time series of remote-sensing images. The challenge of extracting accurate semivariogram curves from objects of long and narrow shapes requires further study, along with the relationship between the scale of segmentation and image spatial resolution, including the type of change and the initial land-cover class.     

基于GeoSOT剖分编码的多尺度空间信息区域包含关系计算方法

针对现阶段不同层次空间信息缺乏联系的问题,提出了一种基于剖分编码的多尺度空间信息间区域包含关系计算方法。首先,以GeoSOT剖分框架为组织基础,在不同尺度层上,以剖分面片作为基本单元组合表示地理对象,依据对象表达多尺度剖分编码模型将地理对象赋予剖分编码。然后,提出了基于剖分编码的区域包含关系计算方法,并给出了算法实现的具体流程。进一步采用剖分信息树状结构对空间信息进行统一管理,为剖分体系下组织与管理不同层次的空间信息提供了一种有效的解决思路。     

基于面向对象的平潭岛大比例尺森林资源监测方法

利用高分辨率遥感影像对森林资源进行大比例尺动态监测可以有效提高林业部门对森林资源管理的时效性。以福州市平潭岛为研究区域,利用高分辨率的WorldView\|2遥感影像,结合大比例尺森林小班矢量图层,基于面向对象的分类思想,采用分层监督分类的方法,提取森林资源变化图斑,实现大比例尺森林资源的动态监测。此种方法有效利用了原始小班边界,快速提取了变化区域,总体分类精度达到了90.85%,表明利用该方法进行大比例尺森林资源变化区域提取是有效可行的。     

基于弱水印的地理数据拓扑完整性检验方法

拓扑关系是空间关系研究的基础问题,被广泛应用在空间查询和空间推理等方面。为了检测地理数据拓扑关系的完整性,提出了一种基于弱水印的地理数据拓扑完整性检验方法。通过地物之间空间相离距离生成水印信息,并修改其相离距离比值,根据比值进行地物缩放以达到嵌入水印的目的。在水印检测时,根据生成的水印与提取的水印的匹配结果来判定地理数据的拓扑完整性。实验结果表明,本方法可以有效地检测出矢量地理数据的拓扑完整性。     

An Object Model of Direction and Its Implications

Direction is an important spatial concept that is used in many fields such as geographic information systems(GIS) and image interpretation. It is also frequently used as a selection condition in spatial queries. Previous work has modeled direction as a relational predicate between spatial objects. Conversely, in this paper, we model direction as a new kind of spatial object using the concepts of vectors, points and angles. The basic approach is to model direction as a unit vector. This novel view of direction has several obvious advantages: Being modeled as a spatial object, a direction object can have its own attributes and operation set. Secondly, new spatial data types such as oriented spatial objects and open spatial objects can be defined at the abstract object level. Finally, the object view of direction makes direction reasoning easy and also reduces the need for a large number of inference rules. These features are important in spatial query processing and optimization. The applicability of the direction model is demonstrated by geographic query examples.     

A database perspective on geospatial data modeling

We study the representation and manipulation of geospatial information in a database management system (DBMS). The geospatial data model that we use as a basis hinges on a complex object model, whose set and tuple constructors make it efficient for defining not only collections of geographic objects but also relationships among these objects. In addition, it allows easy manipulation of nonbasic types, such as spatial data types. We investigate the mapping of our reference model onto major commercial DBMS models, namely a relational model extended to abstract data types (ADT) and an object-oriented model. Our analysis shows the strengths and limits of the two model types for handling highly structured data with spatial components     

基于开源平台构建文献计量地理信息系统

地理信息系统(Geographical Information System,GIS)在文献计量学中的应用是GIS应用拓展的一个新方向,近10年来得到越来越广泛的应用,特别是在空间相关信息展示和查询等方面发展迅速。为了方便非GIS专业人员应用GIS技术进行文献计量信息挖掘、制图和空间分析等操作,利用开源GIS软件DotSpatial开发文献计量GIS工具,可以满足用户数据导入、指标计算、图层和视图操作以及制图输出等需求。研究结果显示:通过研发的文献计量GIS工具可以比较快捷地实现文献指标的空间展示和制图目的。
     

Adaptive local kriging to retrieve slant-range surface motion maps of the Wenchuan earthquake

Kriging is a widely used technique for raster data interpolation from point samples, such as in the generation of digital elevation models and geochemical maps. The quality of the result depends on both spatial distribution of the sampled values and nature of the semivariogram model, which fits an empirical global function to the sample data set to predict values at the unknown locations. However, such a semivariogram model may not be suitable for data sets with complex local trends in spatial distribution, such as those observed in differential interferometric synthetic aperture radar (DInSAR) data of the Wenchuan earthquake. Here we propose a modified kriging method, adaptive local kriging (ALK), for the retrieval of data lost through decoherence in Advanced Land Observing Satellite (ALOS) phased array L-band synthetic aperture radar (PALSAR) DInSAR data, within the intensely deformed fault zone of the 2008 Wenchuan earthquake. In ALK, a series of dynamic linear local semivariogram models is used rather than a global semivariogram for the whole data set. The localized adaptive approach ensures accurate interpolation in the areas of good DInSAR data with small decoherence gaps and avoids drastic errors in the extensive decoherence gaps; the overall value prediction is thus significantly improved, as confirmed by comparison with the original DInSAR data and fidelity verification experiments.