全景图增强地理现实的空间认知问题

基于全景图的增强地理现实平台是一种新的空间认知工具,研究其空间认知问题是进行技术研究的基础。文中介绍全景图增强地理现实的主要技术流程和优势;阐述基于全景图增强地理现实进行空间认知的基础模型;分析全景图和地理信息数据的信息匹配问题,包括比例尺和位置匹配两个方面;介绍地理信息在全景图像上的表达方式,重点解决增强目标和属性信息的选择问题;最后通过与街景地图、虚拟现实平台进行空间认知比较,得出全景图增强地理现实平台的有效性和主要适用范围,表明该平台具有独特的空间认知优势。     

ECM算法的多视SAR影像分割

多视SAR影像像素强度通常建模为Gamma分布,其形状参数为常数(视数)。实验表明,多视SAR影像分割时,设Gamma分布的形状参数为变量可取得更好的分割结果。由于Gamma分布中形状参数以Gamma函数的形式出现,利用EM算法求解时无法获得形状参数的解析解。为此,本文提出了一种基于Expectation/Conditional Maxinnization(ECM)算法的多视SAR影像分割方法。利用ECM算法估计最大化后验概率条件下的Gamma分布参数及表征最优多视SAR影像分割的标号场实现。采用模拟和真实多视SAR影像验证提出算法。实验结果表明,Gamma分布的形状和尺度参数均能快速收敛到稳态值,且以此得到各同质区域的Gamma分布曲线可以很好地拟合其直方图。通过对分割结果的定性和定量分析,可知提出算法具有有效性和可行性,且优于EM算法。     

太原市地表热环境时空格局分析

针对鲜有资源型城市的地表热环境研究这一情况,该文基于Landsat8遥感数据,反演了太原市在2013—2015年间共3期的地表温度。提出一个考虑空间格局结构的城市热岛效应指数,在3期数据中,太原市区整体热岛效应指数分别为9.1%、9.8%和8.9%;同时,根据城市功能和路网结构将太原市区划分为6个分区,从分区级层面量化分析了地表热环境的时空分异特征规律,结果表明,太原市区地表温度整体呈现空间分散化特征,不同分区也呈现出不同的时空格局演变过程;最后,选取40个地表温度实测样本点验证反演精度,最大误差绝对值为1.1℃,最小误差绝对值为0.2℃,均方根误差为0.7℃。     

Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响

Landsat系列卫星上的TIRS热红外传感器数据已被大量应用,针对TIRS数据的地表温度反演也相继开发出一些算法,并有一些研究对TIRS数据的定标及其地表温度反演算法的精度进行了对比。本文主要就TIRS热红外传感器定标参数的变化,结合这些定标参数变化的时间点对有关地表温度反演算法的适用性和有效性进行分析,特别是对劈窗算法是否适合当前的TIRS数据进行了讨论,以使用户能够对Landsat 8 TIRS热红外数据的正确使用有进一步的认识。总的看来,由于视域外杂散光的影响,TIRS数据的定标精度仍达不到设计目标,TIRS第11波段的不确定性仍成倍大于TIRS 10波段。因此,在Landsat团队未彻底解决这一问题之前,同时用TIRS第10、第11这两个差距较大的波段构成的劈窗算法来反演地表温度,其精度存在较大的不确定性,US6-S团队仍在致力于改进第11波段的精度,改进后的波段可以用劈窗策法。目前应以TIRS第10单波段的方式来反演地表温度为宜。     

无人机影像匹配中尺度不变特征应用改进

针对尺度不变特征变换算法用于无人机影像匹配中存在匹配效率低下,误匹配较多等问题,该文提出一种特征点提取优化算法,并改进了特征点匹配策略。通过将原始影像划分格网,依据每个格网影像信息熵的大小来合理分配各格网中特征点数量,实现了基于纹理信息丰富程度的特征点均匀分布;基于Harris兴趣值进行筛选,保留了适合于摄影测量的特征点;采用一种多层次自适应的匹配策略,在尽可能保留正确匹配的同时提高了匹配正确率。基于一对由小型无人机拍摄影像的实验结果表明,所提方法在大量减少SIFT特征点的同时,保证较高的正确率,增加了匹配点的精度,且提高了算法效率。     

Tessellations in GIS: Part II–making changes

Abstract

We attempt to describe the role of tessellated models of space within the discipline of Geographic Information Systems (GIS) – a speciality coming largely out of Geography and Land Surveying, where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps. We look at some of the basic operations in GIS, including dynamic and kinetic applications. We examine issues of topology and data structures, and produced a tessellation model that may be widely applied both to traditional “object” and “field” data types. The Part I of this study examined object and field spatial models, the Voronoi extension of objects, and the graphs that express the resulting adjacencies. The required data structures were also briefly described, along with 2D and 3D structures and hierarchical indexing. The importance of graph duality was emphasized. Here, this second paper builds on the structures described in the first, and examines how these may be modified: change may often be associated with either viewpoint or time. Incremental algorithms permit additional point insertion, and applications involving the addition of skeleton points, for map scanning, contour enrichment or watershed delineation and simulation. Dynamic algorithms permit skeleton smoothing, and higher order Voronoi diagram applications, including Sibson interpolation. Kinetic algorithms allow collision detection applications, free-Lagrange flow modeling, and pen movement simulation for map drawing. If desired these methods may be extended to 3D. Based on this framework, it can be argued that tessellation models are fundamental to our understanding and processing of geographical space, and provide a coherent framework for understanding the “space” in which we exist.     

一种基于灰度块匹配的视野监测算法

针对视野移动方向的判断问题,该文介绍了运动补偿的常用方法及应用,提出了基于灰度块匹配的视野移动监测算法。通过对比各种算法计算复杂度和计算时间,发现灰度投影算法准确率低、计算量较小;块匹配搜索算法中的穷尽搜索算法准确率高,但是计算量较大。为了发挥以上两者算法的优势,将两者结合形成了灰度块匹配算法,并通过实验证实了灰度块匹配算法在实时监测系统中的可行性。     

煤矿地质成图中多边形快速生成算法

针对煤矿地质成图中左转算法生成多边形在复杂的二维图形条件下应用效率低的问题,该文提出了一种新的基于左转算法的多边形快速选取及自动生成算法,实现了2D图形环境中多边形的快速选取及自动生成。通过在进行多边形选取时的起始点,起始方向的选择和对悬挂点、线进行处理,解决了复杂图形下多边形选取及自动生成过程中存在的重复生成、效率低下和搜索回退问题。基于GeoMS3D实现该算法,实验结果表明:该算法逻辑简单、结构清晰,在实际的应用中表现出了较高的运算效率。     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.