Generating and updating multiplicatively weighted Voronoi diagrams for point,line and polygon features in GIS

A Voronoi diagram is an interdisciplinary concept that has been applied to many fields. In geographic information systems (GIS), existing capabilities for generating Voronoi diagrams normally focus on ordinary (not weighted) point (not linear or area) features. For better integration of Voronoi diagram models and GIS, a raster-based approach is developed, and implemented seamlessly as an ArcGIS extension using ArcObjects. In this paper, the methodology and implementation of the extension are described, and examples are provided for ordinary or weighted point, line, and polygon features. Advantages and limitations of the extensions are also discussed. The extension has the following features: (1) it works for point, line, and polygon vector features; (2) it can generate both ordinary and multiplicatively weighted Voronoi diagrams in vector format; (3) it can assign non-spatial attributes of input features to Voronoi cells through spatial joining; and (4) it can produce an ordinary or a weighted Euclidean distance raster dataset for spatial modeling applications. The results can be conveniently combined with other GIS datasets to support both vector-based spatial analysis and raster-based spatial modeling.     

Transformations between Pictures from 2D to 3D

This paper describes programs for 3-dimensional engraving. The programs use raster or vector images to create a 3D model and, subsequently, convert this model into a sequence of control commands for 3D engraving machines. Three programs have been developed. A program for engraving general 3D surfaces from grey-scale images, a program for preparing these grey-scale images from patterns and vector images, and a program for fast 2D engraving. A simple and fast preparation of the 3D model, a user-friendly environment, and small hardware requirements were the principal goals.     

直接从空间数据中挖掘频繁模式

为简化空间频繁模式挖掘的预处理步骤并提高挖掘效率, 提出一种可以直接以空间矢量和栅格图层作为输入的挖掘算法FISA(fast intersect spatial Apriori)。该算法利用图层求交和面积计算操作实现谓词集支持度计数进而实现频繁谓词集和关联规则挖掘。相对于基于事务空间关联规则挖掘算法, FISA不需要预先进行空间数据事务化处理, 并且所得结果均有对应图层, 便于实现结果的可视化; 相对于其他基于空间分析的挖掘算法, FISA支持空间数据的矢量和栅格格式, 且引入了快速求交方法以保证其可伸缩性。实验结果表明该算法可以直接从空间数据中高效正确地挖掘出频繁模式。     

Rock unit discrimination on Landsat TM,SIR-C and Radarsat images using spectral and textural information

The paper presents results for spectral and textural analysis of the rock units in Landsat Thematic Mapper (TM) images, dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR)-C images, and C-band HH polarization Standard Beam 4 and Extended High Incidence Beam 3 Radarsat images from a study area between California and Arizona, USA. Fractal dimension, lacunarity and grey-level co-occurrence matrix (GLCM) textural feature images were created from the SIR-C and Radarsat images. Fractal dimensions were calculated using a differential box counting method and lacunarity measures were obtained using a new grey-scale lacunarity estimation method for 36 sample images extracted from the SIR-C and Radarsat images. The fractal dimension and lacunarity curves and class signature separability analysis show that, for rock unit discrimination using image textural features in the study area, the SIR-C L-HH image is more suitable than other SIR-C images and Radarsat images, and that co-polarization (HH) generally provides more textural information than cross-polarization (HV) in the study area. The study also shows that lacunarity measures can reveal the scaling properties of radar image textures for rock units. The combination of spectral information from Landsat TM images and textural information from radar images improves the image classification accuracy of rock units in the study area.     

Test of a new lacunarity estimation method for image texture analysis

Based on a differential box counting method and a gliding-box algorithm, a new method for estimating the lacunarity of grey scale digital image surfaces is introduced, and directionality of lacunarity defined. To test the performance of the new lacunarity measure, a Brodatz texture image mosaic is employed and several other texture analysis approaches are also applied to the texture mosaic. Quantitative comparison shows that the new lacunarity estimation method for grey-scale images can provide more accurate texture measurements than some existing lacunarity measures, the grey level co-occurrence matrix based texture measures, the Min-Max operator, and the fractal dimension.     

A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks

Spatial clustering analysis is an important issue that has been widely studied to extract the meaningful subgroups of geo-referenced data. Although many approaches have been developed in the literature, efficiently modeling the network constraint that objects (e.g. urban facility) are observed on or alongside a street network remains a challenging task for spatial clustering. Based on the techniques of mathematical morphology, this paper presents a new spatial clustering approach NMMSC designed for mining the grouping patterns of network-constrained point objects. NMMSC is essentially a hierarchical clustering approach, and it generally consists of two main steps: first, the original vector data is converted to raster data by utilizing basic linear unit of network as the pixel in network space; second, based on the specified 1-dimensional raster structure, an extended mathematical morphology operator (i.e. dilation) is iteratively performed to identify spatial point agglomerations with hierarchical structure snapped on a network. Compared to existing methods of network-constrained hierarchical clustering, our method is more efficient for cluster similarity computation with linear time complexity. The effectiveness and efficiency of our approach are verified through the experiments with real and synthetic data sets.     

Comparative analysis of MODIS-FAPAR and MERIS-MGVI datasets: Potential impacts on ecosystem modeling

Assessing the ecosystem productivity dynamics is a key issue in the context of the carbon cycle at the southern Europe ecosystems. The availability of remote sensing data and products, as FAPAR, strongly motivates its use in ecosystem modeling. This paper presents a comparative analysis between MODIS-FAPAR and MERIS-MGVI datasets, two products carrying similar information, from regional (the Iberian Peninsula) to local (six biomes) scales. Moreover, an assessment of the impacts of its use in terrestrial ecosystem modeling, by using the CASA production efficiency model is presented. The analysis was conducted for both datasets for the year 2003, and was supported by an extensive statistical analysis, including geostatistics, focusing both seasonality and spatial coherence. Main findings refer to (i) a systematically higher MODIS than MERIS data, both at the Iberian Peninsula and at local biomes, (ii) a seasonality of the correlation between the two datasets, and (iii) a spatial homogeneity over larger extensions from MERIS-MGVI data, opposing to a higher spatial variability from MODIS-FAPAR. Although these different data properties, it was found that the two independent datasets produce reasonable NEP estimates, by using CASA model at the site level. These results suggest that the MODIS-FAPAR and MERIS-MGVI differences can be minimized in biophysical modeling through model re-parameterization, although this process does not solve the seasonal dependence pattern found in the original datasets.     

A tool for mapping and spatio-temporal analysis of hydrological data

There is a need in water sciences for computational tools to integrate large spatially distributed datasets to provide insight into the spatial and temporal domains of the data while allowing visualization, analysis in the spatial and temporal dimensions, data metrics, and pattern recognition in the same application. Spatial and temporal variability of hydrological processes as well as the associated phenomena transport is better represented in high spatio-temporal resolution datasets. A conceptual data model and analysis tool, SPELLmap, was developed at the USDA Agricultural Research Service, Grazinglands Research Laboratory using the Delphi programming language to rapidly process, manipulate, analyze, and visualize large geo-located datasets. SPELLmap integrates the spatial and temporal domains of hydrological data to perform analyses in space and time while providing data metrics. SPELLmap has the capacity to represent three or four dimensional problems using a layer data structure. Three examples to illustrate SPELLmap functionalities were provided for the raster and raster-to-network domains. SPELLmap can be used for data interpolation, visualization, gridding, pattern recognition, and data metrics in integrated environmental modeling problems.     

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties - the sill and the mean length scale metric - provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R² = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R² = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.     

关系数据库空间栅格数据扩展模型及方法研究

针对海量遥感影像数据有效管理需求,在国产数据库管理系统X-Base内核上,研究并实现空间栅格数据扩展机制。设计基于X-Base关系数据库的空间栅格扩展体系结构,建立了栅格数据扩展体系结构模型,提出基于XML方式的空间栅格数据管理方法。使得扩展后的X-Base数据库能够有效存储、管理空间栅格数据,从而进一步扩展X-Base的功能。     

The learner characteristics,features of desktop 3D virtual reality environments,and college chemistry instruction: A structural equation modeling analysis

We examined a model of the impact of a 3D desktop virtual reality environment on the learner characteristics (i.e. perceptual and psychological variables) that can enhance chemistry-related learning achievements in an introductory college chemistry class. The relationships between the 3D virtual reality features and the chemistry learning test as it relates to the selected perceptual (spatial orientation and usability) and psychological (self-efficacy and presence) variables were analyzed using the structural equation modeling approach. The results supported all the hypothesized relationships except one. Usability strongly mediated the relationship between 3D virtual reality features, spatial orientation, self-efficacy, and presence. Spatial orientation and self-efficacy had statistically significant, positive impact on the chemistry learning test. The results indicate that 3D virtual reality-based instruction is effective for enhancing students’ chemistry achievement. Overall, this study contributed a research model that can help increase the effectiveness of desktop virtual reality environments for enhancing spatial ability and science achievement. Moreover, this study provides insight to science educators, instructional designers, and multimedia developers who are interested in designing science-based instruction using instructional design principles.     

(2+1)D多时空信息融合模型及在行为识别的应用

针对常规的卷积神经网络时空感受野尺度单一,难以提取视频中多变的时空信息的问题,利用（2+1）D模型将时间信息和空间信息在一定程度上解耦的特性,提出了（2+1）D多时空信息融合的卷积残差神经网络,并用于人体行为识别.该模型以3×3空间感受野为主,1×1空间感受野为辅,与3种不同时域感受野交叉组合构建了6种不同尺度的时空感受野.提出的多时空感受野融合模型能够同时获取不同尺度的时空信息,提取更丰富的人体行为特征,因此能够更有效识别不同时间周期、不同动作幅度的人体行为.另外提出了一种视频时序扩充方法,该方法能够同时在空间信息和时间序列扩充视频数据集,丰富训练样本.提出的方法在公共视频人体行为数据集UCF101和HMDB51上子视频的识别率超过或接近最新的视频行为识别方法.     

基于三维栅格的GIS距离分析算法研究与应用

基于距离的分析是GIS中的一项基本空间分析功能,目前该分析主要是基于二维的,对三维空间的距离分析研究则较少。对基于三维栅格的距离分析进行了研究,提出了基于三维栅格的最短距离算法,并把该算法应用于非均质的三维缓冲体的生成。     

基于三维激光雷达的道路边界提取和障碍物检测算法

为了快速有效地提取智能车辆在不同环境下的道路环境信息,提出基于三维激光雷达的道路边界提取和障碍物检测算法.首先,对三维激光雷达点云数据进行栅格化滤波处理,利用单束激光点云空间邻域联合分割的方法进行空间分析,得到点云平滑度特征图像.然后,采用自适应方向搜索算法获取道路边界候选点,并进行聚类分析和曲线拟合.最后,对道路边界约束下可通行区域内点云进行聚类分割,获得道路内障碍物方位和距离信息.实验表明,文中算法能够实时准确地提取道路边界和障碍物位置信息,满足智能车环境建模和路径规划的需求.     

Accurate reconstruction of 3D cardiac geometry from coarsely-sliced MRI

We present a comprehensive validation analysis to assess the geometric impact of using coarsely-sliced short-axis images to reconstruct patient-specific cardiac geometry. The methods utilize high-resolution diffusion tensor MRI (DTMRI) datasets as reference geometries from which synthesized coarsely-sliced datasets simulating in vivo MRI were produced. 3D models are reconstructed from the coarse data using variational implicit surfaces through a commonly used modeling tool, CardioViz3D. The resulting geometries were then compared to the reference DTMRI models from which they were derived to analyze how well the synthesized geometries approximate the reference anatomy. Averaged over seven hearts, 95% spatial overlap, less than 3% volume variability, and normal-to-surface distance of 0.32 mm was observed between the synthesized myocardial geometries reconstructed from 8 mm sliced images and the reference data. The results provide strong supportive evidence to validate the hypothesis that coarsely-sliced MRI may be used to accurately reconstruct geometric ventricular models. Furthermore, the use of DTMRI for validation of in vivo MRI presents a novel benchmark procedure for studies which aim to substantiate their modeling and simulation methods using coarsely-sliced cardiac data. In addition, the paper outlines a suggested original procedure for deriving image-based ventricular models using the CardioViz3D software.     

A multi-level 3D data registration approach for supporting reliable spatial change classification of single-pier bridges

The reliability of condition assessment of bridges using 3D imagery data, such as 3D laser scanning point clouds, relies on inspectors’ structural engineering knowledge and skills of 3D data processing. A challenge of 3D-data-based structural condition assessment lies in the difficulties of reliably comparing 3D imagery data sets collected at different times for analyzing spatial changes of the structures and finding anomalous deformations. Spatial changes of structures could occur at multiple levels of details and be of different types: (1) rigid body motions (e.g., translations and rotations) at the structure or structural element levels; (2) deformations (e.g., bending of girders) at the levels of structural elements. Unfortunately, existing 3D imagery data-based change analysis methods only produce deviations between two 3D data sets without distinguishing deviations caused by various changes at multiple levels. Significant rigid body motions of structures and structural elements often cause large deviations that “overwhelm” deviation patterns caused by smaller element-level deformations so that engineers could hardly recognize local deformations. Unreliable deformation analysis of structural elements can lead to incorrect condition assessments.This paper presents a new multi-level 3D data registration and spatial change classification approach that automate the analysis of both element-level deformations and interactions between the motions of multiple elements based on deviations calculated between two 3D data sets. This approach uses a multi-level data registration method augmented by formalized knowledge for representing spatial changes using deviation maps between two 3D datasets. This knowledge will guide pattern analysis methods to reveal how various changes of structures collectively lead to structural systems behaviors. More specifically, this 3D data registration and spatial change classification approach eliminates deviations caused by rigid body motions before assessing deformations of structural elements. The authors conducted annual 3D imagery data collection for two single pier bridges in July 2015 and June 2016, and use those 3D data to characterize the performance of the new approach in identifying relative motions between and deformations of structural elements. The results indicate that the new approach can reliably identify relative motions between and deformations of bridge elements, such as angular changes between elements, and torsions of girders. Finally, the authors validated the change analysis results generated by the developed approach against the traditional change analysis results obtained by a knowledgeable structural engineering researcher and change analyses in multiple single-pier bridge research studies.     

Analysis of lacunarity and scales of spatial homogeneity in IKONOS images of Amazonian tropical forest canopies

Extensive plot studies across Amazonia have demonstrated that there are large regional gradients in forest productivity and that the dynamics of the forests seem to have accelerated substantially in recent decades, with ensuing impacts on forest structure. Most of these sites are, however, one hectare plots nested within a heterogeneous landscape, and a clear need exists to understand the landscape and regional context of these studies. Remote sensing offers the potential to scale up from plot to higher landscape levels but it has proven complex to evaluate forest structure, and therefore biomass patterns in tropical areas, due to saturation, signal noises, and unclear relationships between reflectance values and structural properties, both for optical and radar systems. In this study, we explore the potential of a textural approach to detect landscape and regional variations in the structure of tropical forest canopies, as viewed from high resolution IKONOS satellite imagery. We used lacunarity analysis and a derived variable, the index of translational homogeneity (ITH), as a tool to search for structural and dynamic forest properties within and among different Amazonian landscapes. The main goals of this research were: (1) to examine the sensitivity and robustness of ITH analysis to details of the analysis procedure; (2) to explore the intra- and inter-regional textural properties of a variety of tropical forest canopies [Caxiuanã, Manaus, Sinop, Santarem (Brazil), and Tambopata (Peru)], and (3) to relate textural properties derived from lacunarity to structural properties of the forest canopy, mainly crown size. Our results show how ITH and lacunarity analyses offer insights into the spatial distribution of structural properties of forest canopies, easily differentiating between terra firme forests and swamp forests. The studied forest canopies are self-similar on length-scales of 5–11 m, and show translational invariance on scales above 20 m (central and western Amazonia) and 30 m (eastern Amazonia) For a restricted range of solar elevation angles, the ITH appears to be determined mainly by the mean size of tree crowns, and by the fraction of large (shadow-generating) trees.