Incorporating neighborhood scale effects into land loss modeling using semivariograms

Journal of Geographical Systems - Scale effects are pervasive in geospatial modeling and affect the reliability of analysis results. This paper examines the neighborhood scale effects on the...     

用邻近索引来加快邻近关系处理的算法研究

针对数据量大、关系复杂情况下的空间邻近关系处理需要大量磁盘I/O的问题,本文提出了一种通过建立空间邻近索引来加快空间邻近关系处理速度的方法,给出了该索引建立、维护的方法以及用该索引实现邻近关系处理的过程。最后从理论上分析了使用和不使用邻近索引时邻近关系处理速度的差异,并用南京公交站点数据来验证,得出了一致的结论。在空间数据引擎的基础上使用邻近索引进行空间邻近关系处理的速度将大大提高。     

Challenges and complications in neighborhood mapping: from neighborhood concept to operationalization

This paper examines complications in neighborhood mapping and corresponding challenges for the GIS community, taking both a conceptual and a methodological perspective. It focuses on the social and spatial dimensions of the neighborhood concept and highlights their relationship in neighborhood mapping. Following a brief summary of neighborhood definitions, five interwoven factors are identified to be origins of neighborhood mapping difficulties: conceptual vagueness, uncertainty of various sources, GIS representation, scale, and neighborhood homogeneity or continuity. Existing neighborhood mapping methods are grouped into six categories to be assessed: perception based, physically based, inference based, preexisting, aggregated, and automated. Mapping practices in various neighborhood-related disciplines and applications are cited as examples to demonstrate how the methods work, as well as how they should be evaluated. A few mapping strategies for the improvement of neighborhood mapping are prescribed from a GIS perspective: documenting simplifications employed in the mapping procedure, addressing uncertainty sources, developing new data solutions, and integrating complementary mapping methods. Incorporation of high-resolution data and introduction of more GIS ideas and methods (such as fuzzy logic) are identified to be future opportunities.     

结合像元空间邻域信息的高光谱影像分类

针对传统的光谱角匹配分类算法仅考虑光谱信息,导致混合像元易出现错分和分类结果中出现"麻点"等问题,该文考虑地物连续性这一特点,提出了一种结合像元空间邻域信息对光谱角进行修正的光谱角匹配分类算法。该方法不仅保留了传统光谱角度匹配算法不受增益因素影响和减弱地形对照度影响等优点,并且减小了混合像元被错分的概率。基于ROSIS获取的Pavia大学校园的高光谱影像分类结果表明:加入像元空间邻域信息后的光谱角匹配算法在保证分类精度的同时,有效地减弱了分类结果中的"麻点"现象,验证了该文方法的可行性、有效性。     

自然邻域支持下的空间同位模式挖掘方法

空间同位模式指频繁发生在邻近空间位置的事件集合,此类模式对于深入理解不同空间要素间的交互关系具有重要意义。空间同位模式挖掘的一个核心内容是空间要素邻近关系构建,然而现有方法在空间要素分布不均匀时难以准确地描述要素间的邻近关系,容易导致挖掘结果的遗漏或误判。为此,本文提出了一种基于自然邻域的空间同位模式挖掘方法。首先从同位模式的产生机理分析入手,过滤同位模式挖掘中的干扰要素;进而,从距离邻近性、密度变化一致性和关系紧密性的原则出发,自适应地构建空间要素实例的自然邻近关系;最后,以自然邻域为基础,基于图的连通性从整体到局部发现多层次同位模式。试验分析与比较发现,本文方法能够有效发现空间要素分布不均匀情况下的同位模式,而且降低了人为设置邻域参数对挖掘结果的影响。     

一种基于邻域数组的区域合并新方法

针对图像分割中的过分割问题,该文提出了一种区域合并方法。该方法在区域合并时采用邻域数组的数据结构来维护区域间的邻接关系,该数据结构相对传统的邻接图具有更高的效率。为了能够综合利用分割对象的各种特征,兼顾区域合并的精度与效率,将合并过程分为两个步骤进行:第一步以区域面积阈值作为合并终止条件,在合并准则中只考虑灰度信息;第二步将灰度阈值作为合并终止条件,并在合并准则中加入了纹理信息。实验结果表明,该方法能够对初分割图像进行高效且精确的合并。     

基于Delaunay三角网的空间邻近关系推理

空间邻近关系是一种重要的空间关系,对这种关系的识别是任何空间建模系统所必须的。Delaunay三角网是根据最小角最大规则建立的一种三角剖分,可以较好地表达空间目标之间的邻近关系。利用Delaunay三角网对空间邻近关系进行了描述,在此基础上提出了空间邻近关系推理方法。     

Expert Classification Method Based on Patch-Based Neighborhood Searching Algorithm

The expert knowledge has been widely used to improve the remotely sensed classification accuracy. Generally, the ex-pert classification system mainly depends on DEM and some thematic maps. The spatial relationship information in pixel level was commonly introduced into the expert classification. Because the geographic objects were found spatially dependent relationship to a certain degree, the commonly used basic unit of spatial relationship information in pixel greatly limited the efficiency of spatial in-formation. A patch-based neighborhood searching algorithm was proposed to implement the expert classification. The homogene-ous spectral unit, patch, was used as the basic unit in the spatial object granularity, and different types of patches' relationship in-formation were obtained through a spatial neighborhood searching algorithm. And then the neighborhood information and DEM data were added into the expert classification system and used to modify the primitive classification errors. In this case, the classi-fication accuracies of wetland, grassland and cropland were obviously improved. In this work, water was used as base object, and different types of water extraction methods were tested to get a result in a high accuracy.     

高光谱遥感图像的空间邻域指数

通过高光谱遥感图像空间邻域内光谱特征的变化,研究了邻域光谱度量指数;根据邻域内端元光谱特征的变化,提出了邻域独立端元指数提取图像的空间维细节信息。通过真实高光谱遥感图像检验,两类邻域指数能够较好地提取高光谱遥感图像中的细节,为进一步结合空间维、光谱维特征的高精度目标探测与识别创造了有利条件。     

顾及地形邻域影响的地质灾害风险易发区评估

地质灾害发生时通常会带来巨大的人力物力损害,而易发区评估是防灾减灾的重要手段.为了尽可能减少灾害带来的经济损失和人员伤亡,本文在GIS的支持下开展地质灾害风险评估研究.具体结合温州市自然地理状况及相关受灾情况历史数据,分析高程因子、坡度因子、坡向因子等地质灾害影响因子,提出了综合邻近环境的地质灾害分析方法,利用层次分析...     

扩展邻域元胞自动机模型城市扩张研究

元胞自动机近年来被广泛应用于动态模拟城市扩张,但其邻域类型一般固定于摩尔邻域.文章基于Geo-CA理论,自定义扩展邻域类型,并结合特定地理位置多层次约束性条件构建CA模型,扩展了CA模型的空间建模功能.以武汉市为例,将2000年和2009年TM遥感影像和城市用地数据作为基础,结合武汉市城市规划图、DEM、道路交通图、水域等多层次图像,构成空间数据库,建立武汉市城市扩张扩展邻域CA模型.最后以2009年数据为基础,对武汉市2018年城市用地扩张情况进行预测,为武汉市城镇建设提供参考.     

基于最优邻域局部熵的点云精简算法

针对传统的点云精简算法中不能良好保留细节特征的问题,提出一种基于最优邻域局部熵的点云精简算法.首先利用点云局部邻域协方差矩阵的3个特征值构造的维度特征,构建局部邻域信息熵函数,其次依据局部熵值最小原则确定最优邻域,然后根据最优邻域下计算的特征值间的关系,以及局部信息熵来剔除平坦区域数据点.通过模拟数据和实例扫描数据精简...     

动态窗口邻域统计的高差计算方法

针对高差对于地貌类型定义、不同地貌类型范围划定以及山地灾害危险性评价的重要意义,该文将坡脚的高程定义为基准高程,提出了动态窗口邻域统计的高差计算方法。计算每个栅格到坡脚的距离,作为该栅格的对应的窗口半径;利用邻域统计的方法,搜索该窗口范围内的最低高程值,即为坡脚的高程,作为该栅格高差计算的基准高程;最终通过高程值与基准高程相减运算,获取每个栅格点对应的高差。选取四川省都江堰市虹口地区为研究区,对比两种不同的高差计算方法。结果显示:在山脊和山肩部位,动态窗口计算的高差值大于静态窗口的高差值,而位于山脚动态窗口计算的高差值小于静态窗口计算的高差值;利用动态窗口获得的高差的精度明显高于静态窗口的计算结果,其平均偏差由75.53降低为13.24。     

基于立体环形邻域的GMRF模型及其在遥感影像纹理识别中的应用

对传统的GMRF模型进行了扩展,建立了基于立体环形邻域的GMRF模型,并设计了分步模型参数求解方法。这种新的模型不仅全面考虑了多光谱高分辨率影像中各波段内像元之间的空间相关性,而且还顾及了波段间像元的相关性。与传统的GMRF模型相比,基于立体环形邻域的GMRF模型提取的纹理信息更为丰富。采用Forrest彩色纹理图像和QuickBird卫星遥感影像进行了实验验证,实验结果表明本文提出的基于立体环形邻域GM-RF模型的纹理识别算法具有较强的普遍适用性,对不同情况的纹理影像(即空间相关性主要存在于波段间还是波段内)进行识别均能获得较好的分类效果。     

Exploring the relationships between perceived neighborhood boundaries and street network orientation

The neighborhood is a core unit of analysis in urban research, planning, and policy-making. However, perceptual and historical processes oftentimes result in neighborhoods that are not tied to sub-urban jurisdictions. For instance, historic neighborhoods might lack official spatial definitions, hampering neighborhood-based tasks in local offices. In this case, urban practitioners can benefit from readily available spatial proxies, such as the local street network. In this study, we conducted an exploratory analysis that combines neighborhood mapping and street network modeling. By retrieving participants' sketched boundaries and quantifying spatial orientations of sketched polygons and local network patterns, we were able to measure and compare the relationships between the urban fabric and the perceived extents of two historic neighborhoods in Lisbon, Portugal. The results provided insights for enhancing existing definitions of non-official neighborhoods, outlining new urban districts as well as for discussions about the role of the urban form in shaping people's perceptions.     

多尺度邻域特征下的机载LiDAR点云电力线分类

利用机载激光雷达技术三维测量精度高且获取快速的优点进行电力线自动分类提取已成为点云数据处理与电力应用的重要领域。针对电力线分类模型的自动化和高精度需求,本文提出了基于三维多尺度邻域特征的机载LiDAR点云电力线分类提取模型框架,主要包括4个步骤：电力线候选点滤波、多尺度邻域类型选取、形状结构特征提取和支持向量机分类。通过对2个复杂城市区域的试验数据集和8种不同邻域类型的详细结果对比分析,发现基于多尺度圆球邻域形状结构特征的分类模型结果准确率、召回率和质量分别达到97%、94%和93%,同时整体处理时间在2个试验数据中分别从366、256 s减少到274、160 s。试验结果表明,该方法在多种复杂城市场景中能够实现机载LiDAR数据的电力线较高精度分类提取。     

基于改进邻域均值的全色影像水域提取

利用传统邻域均值法对全色影像水域进行提取时,所提取的水域边界精确度严重依赖于搜索窗口的大小。本文通过分析水域灰度特征以及提取边界不精确的原因,对方法进行针对性的改进:依据邻域各像素点与种子点的灰度差值分别对其赋予不同的灰度值,以处理后的邻域均值作为特征进行水域提取。实验结果表明:基于改进的邻域均值方法对遥感影像水域有较好的提取结果,提取精度较之改进前有明显改善。     

结合邻域信息和结构特征的遥感影像变化检测

为提高像元级变化检测方法的精度,提出一种结合邻域信息和结构特征的遥感影像变化检测方法.该方法涵盖邻域相关影像(neighborhood correlation image,NCI)、匹配误差和结构特征3种属性特征.首先,通过邻域相关分析技术获得表示上下文信息的邻域相关影像,利用邻域间像素的互相关性进行模板匹配获得匹配误差.然后,基于方向梯度信息提取能抵抗影像间光谱差异的结构特征.随后将邻域相关影像、匹配误差、结构特征作为决策树的分类属性,获取初始变化检测结果.最后,利用马尔可夫随机场(Markov random field,MRF)对其进行优化,获得最终的二值变化图.本文通过采用两组不同传感器的双时相遥感影像进行试验.结果表明,相较于采用变化向量分析法(change vector analysis,CVA)、单一邻域信息法及邻域信息和纹理特征相结合的方法,本文方法有效提高了变化检测的精度.     

Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge

Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. First we present the geometrical features that form the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size to analyze the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Therefore, the method is successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvement for two ICP steps has been noted, and we conclude that our features may not be relevant for very dissimilar object samplings.     

Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5–1.5 m), medium (1.5–6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.