On the estimation of a multi-resolution representation of the gravity field based on spherical harmonics and wavelets

The gravitational potential of the Earth is usually modeled by means of a series expansion in terms of spherical harmonics. However, the computation of the series coefficients requires preferably homogeneous distributed global data sets. Since one of the most important features of wavelet functions is the ability to localize both in the spatial and in the frequency domain, regional and local structures may be modeled by means of a spherical wavelet expansion. In general, applying wavelet theory a given input data set is decomposed into a certain number of frequency-dependent detail signals, which can be interpreted as the building blocks of a multi-resolution representation. On the other hand, there is no doubt that the low-frequency part of the geopotential can be modeled appropriately by means of spherical harmonics. Hence, the main idea of this paper is to derive a combined model consisting of an expansion in spherical harmonics for the low-frequency part and an expansion in spherical wavelets for the remaining medium and high-frequency parts of the gravity field. Furthermore, an appropriate parameter estimation procedure is outlined to solve for the unknown model coefficients.     

'Science', representation and resistance: the Bt cotton debate in Andhra Pradesh, India

Transgenic cotton is promoted in India on the basis that it will improve rural livelihoods, but such claims are contested on the basis that they are 'unscientific'. In this study, discourse analysis is utilized to deconstruct the environmental and scientific narratives employed by two key actors (Monsanto-Mahyco and the Deccan Development Society) in the debate in India. Whilst strong differences in the ideology of the two actors are found to account for their approaches to managing the environment, significant similarities in their approach to science and their recourse to Foucauldian governmentality are also evident. The conclusion considers how the use of discourse analysis could empower the rural poor to take part in the debate in India.     

基于网络模型的等高线群拓扑关系表示

等高线群拓扑关系的提取和表示是基于等高线的空间推理和地形特征挖掘的关键。在顾及地形特征的基础上,本文提出了一种拓扑关系网络来组织地理目标之间的拓扑关系,并进而提出分区二叉树、增量等高线树、等高线拓扑关系网三种拓扑关系网络来组织含未封闭等高线的复杂等高线群的拓扑关系,从而从根本上解决了等高线群的侧向不连续性,使基于等高线的空间推理更加简便和准确。     

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Exploring the relationship between street centrality and land use in Stockholm

This paper examines the relationship between different street centralities and land-use types in Stockholm. Major centrality measures of closeness, betweenness, and straightness are calculated at both global and local levels in both the primary and dual representations of the urban street network. Adaptive kernel density estimation is adopted to transform all unevenly distributed datasets to one continuous raster framework for further analysis. After computing statistical and spatial distribution of each centrality and land-use density map, we find that the density of each street centrality is highly correlated with one type of land use. Results imply that various centralities representing street properties from different aspects can capture the land development patterns of different land-use types by reflecting human activities, and are consequently important indicators to describe urban structure.     

形态分量分析在去除地震资料随机噪声中的应用

以数学形态学和稀疏信号理论为依据,采用形态分量分析(MCA)方法去除地震数据中的随机噪声。应用MCA方法的关键在于选取合适的字典,从地震数据的特点和计算复杂性出发,选取UWT字典和Curvelet字典,一个用来稀疏表示地震数据的局部奇异部分,一个用来稀疏表示地震数据的线状变化部分。采用BCR算法求解目标函数,通过将数据分解为形态特征不同的2个分量,舍弃在字典中不能有效稀疏表示的随机噪声来达到去噪目的。作为一种二维去噪方法,MCA去噪方法在时间和空间方向上都具有很强的随机噪声抑制能力;由于UWT字典和Curvelet字典能够比传统的小波变换有更强的稀疏表示能力,MCA去噪方法对有效信息的损害较小,是一种保真保幅的去噪方法。模型测试和实际资料处理验证了该方法的有效性。     

交通要素启发式多尺度表达技术方法探讨

针对当前地图的单一尺度可视化与用户所需地图的多尺度表达之间的矛盾,利用多尺度制图技术,探讨基于用户制图需求的交通要素启发式多尺度表达的技术方法.通过对用户的制图需求信息进行启发式识别,提取出相应参数对不同尺度下各要素进行取舍和简化,最终实现交通要素的多尺度表达.实践表明,文中方法可以改善电子地图表达尺度单一的缺陷,满足用户对多尺度地图的制图需求.     

光谱与空间维双重稀疏表达的高光谱影像分类

高光谱遥感影像的稀疏分类是当前遥感信息处理的研究热点。本文提出一种光谱与空间双重稀疏表达的高光谱遥感影像分类方法（WSSRC）。首先利用小波字典对光谱维进行稀疏表示,将光谱维稀疏分类转化到小波域稀疏分类;其次,考虑空间邻域地物光谱的统一性和差异性,对邻域内像元分别进行稀疏编码,并对编码进行累加聚合;然后,利用聚合后的稀疏编码构造线性分类器对高光谱影像进行分类;最后,通过2幅标准的高光谱影像数据验证了本文所提出的方法。实验结果表明,该方法能有效地提高影像的分类精度。     

基于单元复形的数字图像多级别层次表达

基于复形理论定义了数字图像空间的拓扑元素及其性质,在此基础上提出一套完备的保持拓扑等价性的层次表达数字图像的数学模型体系框架,并验证了层次表达结构中的Jordan曲线定理.同时,基于单元复形扩展模型,对SPOT影像实施了渐进式离散分割,有效地利用影像蕴含的空间信息,获得了比最大似然分类法更优的分割结果.     

时频误差分析法在地震信号分析中的应用

时频误差分析法与常规的误差分析法相比,既能反映地震信号的振幅误差,也能区分其相位误差.通过连续小波变换的时频误差分析法,分析了平面地震波在复杂构造中含随机介质及其对应构造的各向同性介质模型中的共炮点合成地震记录的时频振幅误差和时频相位误差.研究结果表明,随机介质的振幅比各向同性介质的振幅衰减大,且随机介质的频率也较各向同性介质的频率为宽.