A three-level framework for multi-risk assessment

The effective management of the risks posed by natural and man-made hazards requires all relevant threats and their interactions to be considered. This paper proposes a three-level framework for multi-risk assessment that accounts for possible hazard and risk interactions. The first level is a flow chart that guides the user in deciding whether a multi-hazard and risk approach is required. The second level is a semi-quantitative approach to explore if a more detailed, quantitative assessment is needed. The third level is a detailed quantitative multi-risk analysis based on Bayesian networks. Examples that demonstrate the application of the method are presented.     

WiFi指纹定位的一种新组合算法

探讨AP选取策略和贝叶斯位置估计算法对基于RSS的WiFi室内定位技术位置估计精度的影响。国内外学者分别对AP选取算法和贝叶斯位置估计算法进行了大量的研究。为了进一步深入研究不同算法的优劣性,利用组合优化的思想对不同算法进行组合,通过找出最优算法组合从而提升WiFi室内定位系统的性能。基于互信息最小化的AP选取策略和考虑AP相关性的贝叶斯位置估计算法,提出一种新的WiFi指纹定位组合算法。实验分析表明:新算法具有良好的实用性和定位性能。     

南水北调中线降水丰枯遭遇风险分析

受降水丰枯变化不确定性和差异性的影响,南水北调中线工程水源区与受水区降水的丰枯遭遇状态各不相同,给南水北调工程水资源调度运行带来风险。联合copula函数和贝叶斯网络理论,建立了南水北调中线工程水源区和受水区降水丰枯遭遇风险分析模型,对南水北调中线工程调水最不利的丰枯遭遇风险概率进行了研究。利用copula函数建立了水源区和受水区年降水量联合分布函数,计算条件概率,结合贝叶斯网络进行丰枯遭遇风险分析。结果表明南水北调中线4个受水区调水风险的概率均在25%以下,并对不同情景的调水风险进行了仿真分析。     

Bayesian modelling the retreat of the Irish Sea Ice Stream

We present an 8000‐year history spanning 650 km of ice margin retreat for the largest marine‐terminating ice stream draining the former British–Irish Ice Sheet. Bayesian modelling of the geochronological data shows the ISIS expanded 34.0–25.3 ka, accelerating into the Celtic Sea to reach maximum limits 25.3–24.5 ka before a collapse with rapid marginal retreat to the northern Irish Sea Basin (ISB). This retreat was rapid and driven by climatic warming, sea‐level rise, mega‐tidal amplitudes and reactivation of meridional circulation in the North Atlantic. The retreat, though rapid, is uneven, with the stepped retreat pattern possibly a function of the passage of the ice stream between normal and adverse ice bed gradients and changing ice stream geometry. Initially, wide calving margins and adverse slopes encouraged rapid retreat (～550 m a^?1) that slowed (～100 m a^?1) at the topographic constriction and bathymetric high between southern Ireland and Wales before rates increased (～200 m a^?1) across adverse bed slopes and wider and deeper basin configuration in the northern ISB. These data point to the importance of the ice bed slope and lateral extent in predicting the longer‐term (>1000 a) patterns and rates of ice‐marginal retreat during phases of rapid collapse, which has implications for the modelling of projected rapid retreat of present‐day ice streams. Copyright © 2013 John Wiley & Sons, Ltd.     

利用参考图像的遥感图像模糊估计与恢复

提出一种新的利用参考图像的模糊估计与恢复方法。同一场景不同时相的图像,由于拍摄条件的不同,清晰化程度也往往不同,将高质量的图像作为参考,为模糊估计和恢复提供先验知识,在Bayes统一的框架内进行快速PSF估计,然后使用总变分最小化方法进行恢复。通过真实遥感图像实验,结果表明所提出的方法可以有效地估计退化模糊、恢复未失真图像。     

基于标点随机过程的遥感影像道路提取

在分析贝叶斯方法用于遥感影像目标提取技术的基础上,基于标点随机过程方法,利用线状地物的整体几何约束和地物之间的空间结构及相关关系对目标构建数学模型,提取线状地物,并以道路网的自动提取为例,详细阐述了此算法。     

基于贝叶斯网络分类的遥感影像变化检测

遥感成像过程中,地面、大气等诸多要素的不确定性和波段之间的相关性等原因影响了分类精度,导致变化检测的不准确性。为了提高分类精度往往需要引入先验知识。贝叶斯网络是一种新的数据表达和推理模型,对数据没有严格的正态分布前提要求,通过动态地调整先验概率密度,能有效提高分类精度。以北京通州地区1996-05-29和2001-05-19两个时相的陆地卫星Landsat TM遥感影像为例,介绍了基于贝叶斯网络的分类算法,并在此基础上实现了两个时相遥感影像的变化检测。实验结果表明:基于贝叶斯网络分类算法的后分类比较变化检测方法是遥感影像变化检测的一种新的有效方法。     

基于Membrane MRF模型的SAR图像贝叶斯去斑

SAR图像可以看作是真实反映地物后向散射特性的无噪图像与相干斑噪声的乘积,通过贝叶斯估计从图像观测值估计出图像真值即可去除相干斑.而贝叶斯去斑的关键在于建立能与SAR图像特性相匹配的先验信息模型.用MembraneMRF模型对先验信息建模,克服了以往所用GMRF模型对参数估计十分敏感的问题,并通过对该模型邻域结构的自适应调整来分类处理处于匀质区域和含结构特征区域的像元,在有效抑制相干斑的同时较好地保持图像的结构特征.仿真和实际SAR图像数据的实验结果,验证了所提方法的有效性.     

Bayesian Discovery Process Modeling of the Lower and Middle Jurassic Play of the Halten Terrace,Offshore Norway,as Compared with the Previous Modeling

A Bayesian version of the discovery process model was applied to the pre-rift Lower and Middle Jurassic play of the Halten Terrace, Mid-Norway. The Bayesian approach estimates the lognormal parameters, the discoverability parameter, and the distribution of sizes of the undiscovered fields as well as the play potential, conditioned on a discovery sequence averaging for all possible prior choices weighted by their likelihood. This approach avoids the problem of having to make arbitrary choices for the parameters. The estimates of parameters and play potential based upon the present methodology compares well with previous estimates, if the play is divided into two sub-plays representing the overpressured and normally pressured zones. These sub-plays have been estimated independently and aggregated in order to get the total undiscovered resource potential. This study estimates that the expected remaining play potential is 100 × 10⁶Sm³ o.e., about 9% of the total resources in the play. There is however a 90% chance that the remaining potential ranges from 13 to 282× 10⁶ Sm³ o.e. and a 5% possibility of exceeding this value.     

Comparative assessment of semantic-sensitive satellite image retrieval: simple and context-sensitive Bayesian networks

In recent years, Bayesian networks using unsupervised extracted image features have been applied in many remote sensing information mining systems to enable semantic-sensitive image retrieval. However, a simple Bayesian network insufficiently accounts for the spatial information, that is, the relations among image regions, for the semantic inference process. This drawback significantly impacts the retrieval performance, especially if the utilised features contain no or little spatial information. Therefore, this article proposes a context-sensitive Bayesian network, which infers semantic concepts of image regions based on the spectral and textural characteristics of the regions themselves as well as their contexts, that is, the adjacent regions. In order to compare the context-sensitive Bayesian network with the simple Bayesian network, comprehensive experiments were conducted based on high-resolution multispectral IKONOS imagery. The results show that the incorporation of the image regions' spatial relations not only significantly improves the accuracy of the semantic concepts inference, but also allows more flexibility in choosing the type of low-level features.