基于Surfer Automation对象的DEM应用开发技术

DEM是目前GIS研究的重点,在很多客户应用程序中都涉及到DEM数据的三维显示和分析。但由于DEM三维数据的复杂性,很难在客户程序中编程实现并无缝链入其功能。为此,作者引入了新版Surfer软件提供的新功能-Automation技术,通过例程详细的叙述了基于Automation对象的DEM应用开发技术,突出其编程快速简单,实现功能强大的特点。     

From fields to objects: A review of geographic boundary analysis

Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique for defining objects – geographic boundaries – on spatial fields, and for evaluating the statistical significance of characteristics of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected in the absence of boundary-generating phenomena. Close ties to the object-field dialectic eminently suit boundary analysis to GIS data. The majority of existing spatial methods are field-based in that they describe, estimate, or predict how attributes (variables defining the field) vary through geographic space. Such methods are appropriate for field representations but not object representations. As the object-field paradigm gains currency in geographic information science, appropriate techniques for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic boundary analysis is clearly a valuable addition to the spatial statistical toolbox.? This paper presents the philosophy of, and motivations for geographic boundary analysis. It defines commonly used statistics for quantifying boundaries and their characteristics, as well as simulation procedures for evaluating their significance. We review applications of these techniques, with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the implementation of these methods within geographic boundary analysis software: GEM. Received: 22 March 1999 / Accepted: 7 September 1999     

Principal component analysis in application to object orientation

This paper proposes a new method based on principal component analysis to find the direction of an object in any pose.Experiments show that this method is fast,can be applied to objects with any pixel distribution and keep the original properties of objects invariant.It is a new application of PCA in image analysis.     

高分辨率影像城市绿地快速提取技术与应用

高分辨率遥感影像是城市绿地信息快速提取的主要数据源 ,文中以多尺度影像分割与面向对象影像分析方法为主要技术 ,利用样本多边形对象的成员函数建立训练区 ,自动提取大庆市城市绿地覆盖信息 ,达到清查城市绿地的目的。该方法信息获取周期短、精度高、成本低 ,实现了城市绿地信息精确获取与快速更新。     

路网表达目标选取的内插方法研究

针对任意未知比例尺下路网表达目标派生时的目标选取问题,集成现有方法并进行拓展,提出体现多尺度表达目标间关联信息的传递和继承特性、顾及路网多尺度表达属性影响的目标“内插”方法。阐述内插法选取路网表达目标的基本思想和技术路线,定义内插法的综合权重函数,设计并实现内插法派生路网目标的试验方案和技术流程。试验表明该法既继承较小比例尺下的表达目标,又客观评估各种因素对路网目标选取的影响程度,保证派生目标的客观选取。     

实现遥感相机自主辨云的小波SCM算法

随着遥感相机分辨力的提高和幅宽的增大,星上固存和数传带宽面临巨大的挑战。提出一种多分支云判别算法,可控制相机在有云区关机停拍。首先利用计算量较小的光谱阈值判别法对云和地物粗略分类,在不能确定云或地物时,采用纹理分析方法判别。为减小误判可能,算法采用小波SCM提取纹理特征,并提出一种基于ASM和熵的双判别方式。通过对24．5幅遥感图像进行试验验证,证明该算法能快速准确识别云层和地物,总误判率小于5％。     

不确定线-面拓扑关系的描述与判别

不确定线状目标和面状目标之间拓扑关系的描述是空间信息处理过程中经常面对的问题。在对不确定线状目标和面状目标进行描述的基础上,对不确定线状目标与面状目标各组成部分之间的相交程度进行了定量表达,通过计算这些度量组成的空间向量与9-交集模型确定的空间关系向量之间的相关度,提出了一种不确定线状目标和面状目标之间拓扑关系的描述模型,通过定量的方法来对其空间拓扑关系进行判别。     

基于小波分析的成像光谱图像随机点噪声消除

成像光谱技术为实现遥感定量分析、识别地物提供了一种重要的有效技术手段,但是成像光谱图像中往往含有严重程度不一的随机点噪声。要优化成像光谱图像质量,提高依据地物光谱特征探测、识别地表物地有效性,必须将图像中的随机点噪声予以消除或压抑。在分析已有各种随机点噪声消除或压抑方法的基础上,提出了一种基于小波分析理论的成像光谱图像随机点噪声消除方法。对成像光谱图像的试验处理及与邻域平均、中值滤波去噪声处理方法     

基于灰集的不确定性时空数据模型

不确定性处理是时空数据库面临的新问题。其研究的首要任务是对不确定时空对象进行建模,研究时空不确定性的表示和存储,进而设计离散模型实现数据库系统。提出了建立在灰集理论上的时空数据抽象模型,适合处理部分已知部分未知的不确定对象。首先给出了抽象数据类型的定义,包括建立在灰集理论上的基本数据类型,空间数据类型和时空数据类型;随后对不确定时空分析操作进行了简单的定性分析;最后给出不确定性时空查询的表达方式。     

一种获取野外实测目标物BRDF的方法

目标物的二向性反射特性,无论是在遥感模型还是在遥感反演中都扮演着重要的角色,因此,选用正确的方法获得自然条件下目标物的二向性反射特性是遥感定量分析的基石。长期以来,人们往往用在自然条件下（即太阳直射光和天空漫射光同时存在）测得的双向反射率因子（Bidirectional Reflectance Factor,BRF）作为对目标物反射特性的表述,但定义BRF时,对外来辐射环境（即入射辐射亮度的空间分布函数）没有做出明确的规定,事实上,这样测得的BRF值与辐射环境有关,用它来描述目标物的反射特性是不妥当的。本文通过野外测量证实了上述观点,还证明了双向反射率分布函数（Bidirectional Reflectance Distribution Function,BRDF）与辐射环境无关的事实。同时本文也为读者提供了一种获得野外实测目标物BRDF的方法。实验证明,使用该方法能较准确地获得目标物在自然条件下的BRDF。     

Spatial econometrics functions in R: Classes and methods

 Placing spatial econometrics and more generally spatial statistics in the context of an extensible data analysis environment such as R exposes similarities and differences between traditions of analysis. This can be fruitful, and is explored here in relation to prediction and other methods usually applied to fitted models in R. Objects in R may be assigned a class attribute, including fitted model objects. Such fitted model objects may be provided with methods allowing them to be displayed, compared, and used for prediction, and it is of interest to see whether fitted spatial models can be treated in the same way. Received: 26 August 2002 / Revised version: 15 January 2003     

Video camera registration using accumulated co-motion maps

The paper presents a method to register partially overlapping camera-views of scenes where the objects of interest are in motion even if unstructured environment and motion. In a typical outdoor multi-camera system the observed objects might be very different due to the changes in lighting conditions and different camera positions. Hence, static features such as color, shape, and contours cannot be used for camera registration in these cases. Calculation of co-motion statistics, which is followed by outlier rejection and a nonlinear optimization, does the matching. The described robust algorithm finds point correspondences in two camera views (images) without searching for any objects and without tracking any continuous motion. Real-life outdoor experiments demonstrate the feasibility of our approach.     

像斑直方图相似性测度的高分辨率遥感影像变化检测

基于像斑的变化向量分析法CVA(Change Vector Analysis)过分依赖像斑的灰度均值信息,而未能有效利用其灰度分布信息,这在高分辨率遥感影像变化检测中存在不足。本文提出了一种基于像斑直方图相似性测度的变化检测方法。利用G统计量构建不同时期像斑之间的相似性测度。假设所有像斑的相似性测度值符合混合高斯分布模型,通过期望最大化算法EM(Expectation Maximization)求解相关参数,最后采用基于最小错误率的贝叶斯判别规则获取最终的变化结果。实验表明,本文提出的上述方法能够有效提高变化检测的精度。     

面向对象的高分辨率遥感影像阴影提取方法

针对高分辨率遥感影像阴影提取的问题,通过分析影像主成分变换和HIS变换的特征,提出了一种面向对象的高分辨率遥感影像阴影提取方法:首先,使用mean shift分割方法进行影像去噪和平滑;然后,结合主成分变换和HIS变换形成了一种阴影检测指数(SDI);最后,通过阈值分割提取阴影信息。选取两景影像进行了阴影提取实验。实验结果表明,SDI能有效地区分阴影与建筑物、水体、蓝色地物、植被等非阴影地物;另外,mean shift分割能有效地去除结果中斑点噪声的影响,提高阴影检测精度。     

面向全时段查询的移动对象时空数据模型研究

大量移动对象时空数据的有效管理是移动空间信息服务的一个新兴的研究内容。本文提出一个面向全时段查询的移动对象时空数据模型,并给出基本的模型定义;探讨移动对象过去、现在和将来的运动位置估算方法,这些方法包括延时法、滑动平均法、三次指数法等;研究了移动对象的状态更新策略,提出一个适应于更新时间间隔的变动阈值法的移动对象的状态更新策略,该策略采用时间滑动窗口形式建立移动对象状态更新间隔的一元指数回归方程,通过求取回归参数获得移动对象状态更新趋势,利用该参数动态调整状态更新闽值。最后,给出实验模拟结果,并对今后的研究工作进行了展望。     

Measuring Dynamic Interaction in Movement Data

The emergence of technologies capable of storing detailed records of object locations has presented scientists and researchers with a wealth of data on object movement. Yet analytical methods for investigating more advanced research questions from such detailed movement datasets remain limited in scope and sophistication. Recent advances in the study of movement data has focused on characterizing types of dynamic interactions, such as single‐file motion, while little progress has been made on quantifying the degree of such interactions. In this article, we introduce a new method for measuring dynamic interactions (termed DI) between pairs of moving objects. Simulated movement datasets are used to compare DI with an existing correlation statistic. Two applied examples, team sports and wildlife, are used to further demonstrate the value of the DI approach. The DI method is advantageous in that it measures interaction in both movement direction (termed azimuth) and displacement. Also, the DI approach can be applied at local, interval, episodal, and global levels of analysis. However the DI method is limited to situations where movements of two objects are recorded at simultaneous points in time. In conclusion, DI quantifies the level of dynamic interaction between two moving objects, allowing for more thorough investigation of processes affecting interactive moving objects.     

基于Delaunay三角关系的面状要素合并方法

详细介绍了一种基于Delaunay三角关系的面状要素合并的新方法，并举例阐述了该方法在居民地、植被和土质等面状要素的制图综合中的应用。     

软件复用的数化方法研究——以GIS应用领域软件工程为例

提出了领域软件复用的数化方法，对软件数化的概念、特征和实现方法进行了阐述，构建了软件数化元模型。以OfficeGIS领域软件为例，给出了在GIS应用领域进行软件数化和复用的一个实例。     

目标保真的背景影像高倍率压缩

传统的分类压缩方法由于对背景采取一致性的有损压缩，在高压缩后不能保留细节，因此在图像重建时背景不能提供很多的信息，整图重建的效果取决于目标本身的分割质量。本论文在将目标提取后，再对背景进行细分类，保留与目标关系密切的部分背景，并把这部分区域与已分割出的目标划为同一类，其余背景划为一类，进行分类别压缩。这种方法允许分割的目标近无损，能增强目标的可理解性。     

多层特征与上下文信息相结合的光学遥感影像目标检测

目标检测是遥感影像分析的基础和关键。针对光学遥感影像中目标尺度多样、小目标居多、相似性及背景复杂等问题,本文提出一种将卷积神经网络(CNN)和混合波尔兹曼机(HRBM)相结合的遥感影像目标检测方法。首先设计细节—语义特征融合网络(D-SFN)提取卷积神经网络低层和高层融合特征,提升目标特征的判别力,特别是小目标;其次考虑上下文信息对目标检测的影响,结合上下文信息进一步加强目标表征的准确性,提升检测精度。在NWPU数据集上试验表明,本文方法能够显著提升目标检测精度且具有一定程度的稳健性。