带多个空洞的面对象间拓扑关系的层次组合模型

空间拓扑关系描述是空间关系的主要内容,是与人类的认知概念一致的,在空间数据查询与挖掘等方面有重要应用。很多学者研究了不带空洞的面对象间的拓扑关系,但对于带空洞的面对象间的拓扑关系研究甚少。首先回顾了现有模型,并指出了各模型的优缺点,然后根据简单面对象的8种基本空间拓扑关系,对带多个空洞的面对象的拓扑关系进行了层次组合分析,提出了一种能描述带多个空洞的复杂面对象间的拓扑关系的层次组合模型。该模型能描述带多个空洞的复杂面对象间的所有拓扑关系,而且不因面对象中空洞的编号顺序不同导致模型所描述的结果不同,同时也弥补了4-4ID模型只能描述带一个空洞的面对象的不足。     

面向对象的遥感影像多层次迭代分类方法研究

在分析应用对象化分析方法改进高空间分辨率遥感影像分类技术的基础上,提出了应用多层次的迭代模型改进分类流程,在自适应的迭代过程中有效地结合主导类别选择、高级对象特征计算、基于互信息的特征选择等技术提高对象化方法中丰富的影像特征的利用效率,同时,有机结合像素级特征信息弥补对象化特征。通过对SPOT5影像与航空影像两种数据源...     

系统辨识(4)：辅助模型辨识思想与方法

辅助模型辨识思想、多新息辨识理论、递阶辨识原理、耦合辨识概念是该文作者提出的研究辨识问题的原创性新方法,已经被用在很多辨识问题的研究中,形成了不同的辨识方法族,可以用于解决许多线性或非线性模型的自适应信号处理、自适应参数估计、自适应滤波和预测、自适应控制等问题.由于客观事物具有双重属性:一些特征变量是可观测的;一些是不可测的.如果表征系统特征的观测变量都是可测的,就容易建立描述其运动规律的数学模型.客观事物的不可测属性给建立系统数学模型带来特别的困难.在这种情况下,如何利用系统的可测信息,实现对系统未知变量的估算,来建立系统的数学模型,是辨识领域极具挑战性的研究课题.辅助模型辨识思想就是在这样的背景下发展起来的.该文介绍辅助模型辨识思想和一些基于辅助模型的辨识方法.     

Contribution of glacier meltwater to streamflow in the Wind River Range,Wyoming, inferred via a Bayesian mixing model applied to isotopic measurements

The Wind River Range (WRR) of Wyoming has the largest concentration of alpine glaciers in the American Rockies and contributes to several major river systems in the western United States. Declines in the areal extent and volume of these glaciers are well documented, and eventual loss of alpine glaciers will reduce the amount of water available for agricultural and domestic use. The contribution of glacial melt to streamflow remains largely unquantified in Wyoming. We used isotope measurements and Bayesian modeling to estimate the fractional contribution of glacier meltwater to Dinwoody Creek (DC) in the WRR on bi‐weekly and seasonal (spring, summer, and fall) time scales over 2 years. In 2007 and 2008, we made temporally intensive measurements of the stable isotope composition of water from the DC watershed. Samples of the primary sources of streamflow (snowmelt, glacier melt, rain, and baseflow) were collected during field campaigns, and automated collection of stream samples occurred over the melt season. Isotope data (D and ¹⁸O) were analyzed within a hierarchical Bayesian framework that incorporated temporal and spatial correlations. Glacial melt contributed a significant proportion (～53–59%) to streamflow in a low‐flow year (2007) or when streamflow was low during a high‐flow year (2008). In 2008, a large and persistent snowpack contributed significantly (～0·42–51%) to streamflow in mid‐summer. The large contribution of glacial melt to streamflow suggests that the loss of glaciers may impact riparian ecosystems and human water supplies in the late summer and in years with low snowpack. Copyright © 2011 John Wiley & Sons, Ltd.     

基于周期表的时空关联规则挖掘方法与实验

地理现象的周期性往往掩盖了许多地学规律,这也是地学数据挖掘的一个主要内容.本文以周期表设计了一种时空层次关联规则挖掘方法--PRules-Miner.模型利用周期表的表现形式对时空数据进行组织,并通过两步挖掘过程发现具有"遥相关"地理事物间的变化模式.模型算法分为3个步骤:(1)过滤周期表内无序数据:逐行地提取多周期内...     

巴丹吉林沙漠湖泊水化学空间分带性与湖泊面积的等级关系

依据Q-型系统聚类分析方法,选取巴丹吉林沙漠湖泊水化学离子为变量,将研究区划分为A、B、C三个分带（区）。同时,利用遥感影像资料提取相应湖泊的面积信息,并进行等级划分,与湖泊水化学特性作对比分析,以探究二者的关系。结果表明：位于沙漠东南边缘的A类湖泊沿东北-西南向呈带状分布,以淡水湖和微咸水湖为主,湖泊面积极小;B类湖...     

环渤海地区旅游合作分类发展研究

环渤海地区旅游业发展存在差距性,使得各城市在合作中处于不同的层级。论文基于帕累托改进分析,将旅游合作划分为合作自由区、合作滞后区、合作困难区3个层次,之后通过系统聚类确定环渤海地区各城市所处的层次,并从合作策略、运行保障机制、空间合作机制上提出各城市在旅游合作中的路径选择。     

聚类分析在地下水水质分类评价中的应用

为更好的探究聚类分析在地下水环境评价中的应用,结合重庆市藻渡水库工程,采用系统聚类法分析了水库枢纽和库区位置的地下水监测点与周围煤矿污染源之间的相互关系,运用灰色聚类法和改进的灰色聚类法对地下水监测点进行综合评价,并分析了两种方法的优缺点。最终将17个地下水监测点划分为未超标天然地下水和受煤矿开采污染的地下水两大类,分析了污染物来源并评价了污染程度,为藻渡水库工程的地下水污染防治提供了科学依据。     

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Abstract

Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.     

美国内布拉斯加州林肯市犯罪行为的聚类及热点分布分析

抢劫和入室盗窃犯罪行为是日常生活中最长见的违法行为,而这两种违法行为的发生有一定的规律可循。例如抢劫行为主要发生在市中心、CBD和商业中心的人群、商铺密集区域,而入室盗窃则多发生于生活社区及其周边地带。然而,想要防范这两种犯罪行为需要根据实际情况进行分析和比对,结合分析结果提出准确合理的警力部署。美国内布拉斯加州林肯市警察局针对该市抢劫和入室盗窃犯罪行为高发的情况,需要针对具体分析结果对于警力进行部署,主要针对徒步巡逻和是否需要对住户进行入户警示进行分析,根据分析结果进行具体部署。本文中使用的分析方法为紧邻层空间聚类分析(NNH)和针对犯罪的空间和时间分析法(SPAC),分别对林肯市入室行窃和抢劫的数据进行分析并尝试将两种方法进行对比以便给出更加科学的分析报告。