Integration of linear and areal hierarchies for continuous multi-scale representation of road networks

Spatial data can be represented at different scales, and this leads to the issue of multi-scale spatial representation. Multi-scale spatial representation has been widely applied to online mapping products (e.g., Google Maps and Yahoo Maps). However, in most current products, multi-scale representation can only be achieved through a series of maps at fixed scales, resulting in a discontinuity (i.e., with jumps) in the transformation between scales and a mismatch between the available scales and users' desired scales. Therefore, it is very desirable to achieve smoothly continuous multi-scale spatial representations. This article describes an integrated approach to build a hierarchical structure of a road network for continuous multi-scale representation purposes, especially continuous selective omission of roads in a network. In this hierarchical structure, the linear and areal hierarchies are constructed, respectively, using two existing approaches for the linear and areal patterns in a road network. Continuous multi-scale representation of a road network can be achieved by searching in these hierarchies. This approach is validated by applying it to two study areas, and the results are evaluated by both quantitative analysis with two measures (i.e., similarity and average connectivity) and visual inspection. Experimental results show that this integrated approach performs better than existing approaches, especially in terms of preservation of connectivity and patterns of a road network. With this approach, efficient and continuous multi-scale selective omission of road networks becomes feasible.     

Fuzzy ontology-based foreshore identification from digital terrain models and very high-resolution airborne imagery through GEOBIA multi-scale analysis

ABSTRACT

Coastal mapping provides information for coastal zone protection. Land foreshore-border mapping is important, since it is related to land and property management, raising the need for an automated foreshore delineation framework. This research develops a multi-scale GEographic Object-Based Image Analysis method for foreshore identification from single-date very high-resolution imagery and DTM. Foreshore interpretation criteria were provided by the Greek Cadastral Office; however, they defined the foreshore-borders in an ill-defined manner. Thus, an ontology was designed and implemented to formalize the implicit spectral, geometric, and spatial relationships described in the interpretation criteria, and employ them during identification. The final ontology was delivered after three design phases: specification, conceptualization, and knowledge formalization. Imagery preprocessing through anisotropic morphological levelings reduced undesired spatial detail and noise. A three-level segmentation and ontological classification approach was developed to compute proper objects and assign them into semantic categories. Class definitions were created through fuzzy restrictions, representing value ranges of the employed properties. Evaluation of the extracted foreshore area with human-interpreted reference data showed a 77.9% quality, which was satisfactory. Evaluation by comparing the extracted foreshore border to the reference data showed an average deviation of 2.4 m, which is within Greek Cadastral Office specifications.     

Approximation approach to the SRM based on root decomposition in the simulation of spatially varying ground motions

The spectral representation method(SRM) is widely used to simulate spatially varying ground motions.This study focuses on the approximation approach to the SRM based on root decomposition,which can improve the efficiency of the simulation.The accuracy of the approximation approach may be affected by three factors: matrix for decomposition,distribution of frequency interpolation nodes and elements for interpolation.The influence of these factors on the accuracy of this approach is examined and the following conclusions are drawn.The SRM based on the root decomposition of the lagged coherency matrix exhibits greater accuracy than the SRM based on the root decomposition of the cross spectral matrix.The equal energy distribution of frequency interpolation nodes proposed in this study is more effective than the counter pith with an equal spacing.Elements for interpolation do not have much of an effect on the accuracy,so interpolation of the elements of the decomposed matrix is recommended because it is less complicated from a computational efficiency perspective.     

基于地震信号波形形态差异的面波噪声稀疏优化分离方法

实际地震信号通常可表示为具有波形特征差异的多种基本波形信号的线性组合,如叠前道集中的工频干扰噪声与有效波信号、面波噪声与体波信号等.选择单一数学变换方法,往往不易实现地震信号的稀疏表示.近年来发展的形态成分分析理论,通过联合多种数学变换,可实现对复杂信号的稀疏表示.本文根据单道地震记录中面波与体波信号波形结构特征的差异性,提出一种基于形态成分分析的面波噪声衰减方法.针对面波的低频、窄带以及频散特性选择一维平稳小波变换作为其稀疏表示字典,而针对体波波形的局部相关特性选择局部离散余弦变换作为其稀疏表示字典,建立基于双波形字典的形态成分分析模型,通过求解该稀疏优化问题获得最终的信噪分离结果.理论模型和实际地震资料处理证实该方法不仅能够衰减单炮地震记录中的强面波干扰噪声,同时能够更好地保护有效信号的波形特征与频谱带宽,为地震资料的后续处理和分析提供良好的数据基础.     

Exploring the reasons for the seasons using Google Earth, 3D models,and plots

ABSTRACT

Public understanding of climate and climate change is of broad societal importance. However, misconceptions regarding reasons for the seasons abound amongst students, teachers, and the public, many of whom believe that seasonality is caused by large variations in Earth’s distance from the Sun. Misconceptions may be reinforced by textbook illustrations that exaggerate eccentricity or show an inclined view of Earth’s near-circular orbit. Textbook explanations that omit multiple factors influencing seasons, that do not mesh with students’ experiences, or that are erroneous, hinder scientifically valid reasoning. Studies show that many teachers share their students’ misconceptions, and even when they understand basic concepts, teachers may fail to appreciate the range of factors contributing to seasonal change, or their relative importance. We have therefore developed a learning resource using Google Earth, a virtual globe with other useful, weather- and climate-related visualizations. A classroom test of 27 undergraduates in a public research university showed that 15 improved their test scores after the Google Earth-based laboratory class, whereas 5 disimproved. Mean correct answers rose from 4.7/10 to 6/10, giving a paired t-test value of 0.21. After using Google Earth, students are helped to segue to a heliocentric view.     

A hierarchical spatiotemporal adaptive fusion model using one image pair

ABSTRACT

Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past decade, although many advances have been made in the spatiotemporal fusion models, there still remain several shortcomings in existing methods. In this article, a hierarchical spatiotemporal adaptive fusion model (HSTAFM) is proposed for producing daily synthetic fine-resolution fusions. The suggested model uses only one prior or posterior image pair, especially with the aim being to predict arbitrary temporal changes. The proposed model is implemented in two stages. First, the coarse-resolution image is enhanced through super-resolution based on sparse representation; second, a pre-selection of temporal change is performed. It then adopts a two-level strategy to select similar pixels, and blends multi-sensor features adaptively to generate the final synthetic data. The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change. Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases.     

Effectiveness of Cartogram for the Representation of Spatial Data

Abstract

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Cartogram is a technique for visualisation of the geographical distribution of spatial data. It has two main types, i.e. distance cartogram and area cartogram. Area cartogram is a transformed map in which areas are resized in proportion to an attribute value. A number of techniques have been developed for the generation of area cartograms. Some researchers consider cartogram as a very effective technique for visualisation of spatial data, while others doubt about the effectiveness because of the possible distortion in shape and/or disconnectivity in topology. This study aims to evaluate the effectiveness of area cartogram for visualizing spatial data. In this study, two comparative experiments have been conducted. One is the comparison between thematic maps and cartograms, and the other is the comparison among different types of area cartogram. Two sets of data with different characteristics are used, i.e. 2005 China population data and 1996 US election data. Results show that cartogram is more effective in the representation of the 1996 US election data which provides a qualitative result (i.e. in binary form or nominal data), but thematic map is far more effective in the representation of 2005 China population data which provides a quantitative result (in classes or ordinal data). It is also found that among different types of area cartogram, pseudo-cartogram is the most preferred technique and Dorling cartogram is the least preferred one.     

Scree Representation on Topographic Maps

Abstract

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Scree patterns are an important element of mountain maps in Swiss style. The size and density of scree dots vary with the exposition towards a source of illumination, which makes the dots extremely labour intensive to map without specialized algorithms. This paper identifies design principles for the symbolisation of scree fields on mountain slopes and presents a digital method for the quick placement of dot symbols requiring only minimal interventions by a cartographer. When digitally produced scree is combined with a shaded relief and a rock drawing, the terrain appears as a continuous three-dimensional surface to the reader. The described method is implemented in Scree Painter, a specialized free open-source software application. Scree patterns produced with Scree Painter match the quality standards of manually generated scree representations.     

Overlapped rectangle image representation and its application to exact legendre moments computation

Linear quadtree is a popular image representation method due to its convenient imaging procedure. However, the excessive emphasis on the symmetry of segmentation, i.e. dividing repeatedly a square into four equal sub-squares, makes linear quadtree not an optimal representation. In this paper, a no-loss image representation, referred to as Overlapped Rectangle Image Representation (ORIR), is presented to support fast image operations such as Legendre moments computation. The ORIR doesn’t importune the symmetry of segmentation, and it is capable of representing, by using an identical rectangle, the information of the pixels which are not even adjacent to each other in the sense of 4-neighbor and 8-neighbor. Hence, compared with the linear quadtree, the ORIR significantly reduces the number of rectangles required to represent an image. Based on the ORIR, an algorithm for exact Legendre moments computation is presented. The theoretical analysis and the experimental results show that the ORIR-based algorithm for exact Legendre moments computation is faster than the conventional exact algorithms.     

Wide-band multi-spectral space for color representation

This paper develops a wide-band multi-spectral space for color representation with Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matching method aims at improving the accuracy of color representation as well as reducing costs for processing and storing multi-spectral images. The results show that the space can present our experimental original spectral spaces (i. e. Munsell color matt and DIN-6164 color chips) with high efficiency, and that the spanning space with three eigenvectors can present the original space at more than 98% CSCR, and when 5 eigenvectors are used it can cover almost the whole original spaces.