一种结合能量最小和马尔可夫随机场的图像分割方法

提出一种新的基于马尔可夫随机场(Markov Random Field,MRF)的图像分割算法。根据Gibbs分布与MRF的等价性,图像分割问题转换为后验能量函数最小化所对应的标号问题。该文采用图割技术的-αexpansion算法进行后验能量函数的局部最优化,并通过近似于最大期望(EM)算法的迭代过程估算数据模型中的参数。对合成图像和遥感图像的分割实验表明,该方法的运算时间和分割精度都能达到满意的效果。     

Bayesian Analysis of the Discovery Process Model Using Markov Chain Monte Carlo

Based upon the Bayesian framework for analyzing the discovery sequence in a play, a Markov chain Monte Carlo sampler—the Metropolis–Hastings algorithm, is employed to sample model parameters and pool sizes from their joint posterior distribution. The proposed sampling scheme ensures that the parameter space of changing dimension can be traversed in spite of the unknown number of pools. The equal sample weights make it easy to obtain the confidence intervals and assess the statistical error in the estimates, so that the statistical behaviors of the discovery process modeling can be well understood. Two application examples of the Halten play in Norwegian Sea and the Bashaw reef play in the Western Canada Basin show that, the computational advantage of this method to the simple Monte Carlo integration is considerable. In order to increase the convergence speed of the sample chains to the posterior distributions, several parallel simulations with different starting values are recommended.     

A Branching Poisson-Markov Model of Earthquake Occurrences

Summary. The Markov process is re-examined as a possible model for aftershock occurrences. In this model, ɛ the state variable is assumed to be the accumulated strain energy. The transition in the energy state is related directly to the magnitude of the aftershock. The known empirical relations on the decay of aftershock sequences and frequency-magnitude law, are incorporated in determining suitable functions for the rate [λ(ɛ)] and transition probabilities [ T ( X |ɛ)] of the Markov process. A computer simulation of the process using a random number generator verified that the empirical relations were properly duplicated with these functions.
To model a complete earthquake catalog, including the main events and aftershocks, two processes are combined by assuming that: (1) independent earthquakes occur as a stationary Poisson process, and (2) they trigger aftershock sequences by channelling a fixed portion of their energy into the Markov process. A synthetic earthquake-aftershock catalogue is generated by simulating the branching Poisson-Markov process and is found to be fairly realistic.     

嵌入式马尔科夫链的地质属性建模与应用

传统的地质统计学所形成的算法理论和方法(如克里格算法)过分依赖样品的数据,变异函数参数众多,给地质模拟造成很大困难。基于马尔科夫链的地质属性建模采用转移概率描述样品区域的各种参数变量,通过转移概率矩阵直接推导地质属性分布比例、平均长度,其简化了地质空间各向异性处理过程,克服了传统地质统计学中参数众多且复杂难以计算和地质体分布过程中存在不对称性等缺陷,使得整个地质属性建模的过程更简洁、清晰,容易理解,且建模的结果可以很好地反映地质体空间分布的复杂性。该文利用马尔科夫链对南京市河西地区的新近地质层进行了地质属性建模,实例应用表明,使用该模型进行地质属性建模可为进一步的数值模拟提供支持。     

Spatio-temporal Rainfall Distribution and Markov Chain Analogue Year Stochastic Daily Rainfall Model in Ethiopia

This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984-31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM.     

干旱区绿洲LUCC马尔柯夫过程分析——以新疆于田绿洲为例

土地利用/覆盖变化(LUCC)在干旱区环境变化及生态监测中具有重要意义。本文选取塔里木盆地南缘典型绿洲于田县,利用TM遥感影像,基于Markov模型预测了于田绿洲未来的发展趋势,得出绿洲将向良性方向发展的结论。通过对Markov模型中不同步长收敛结果的讨论,可以看出间隔10 a1、2 a步长的马尔柯夫过程与间隔两年的马尔柯夫过程有着明显的不同,短时间序列(两年)具有很大的随机特性,说明马尔柯夫模型中时间尺度是一个重要环节。由本文研究可以看出,基于遥感的Markov模型可以分析绿洲的变化趋势,在干旱区绿洲LUCC及其环境动态监测中具有重要的实用意义。     

拉萨地区土地利用变化情景分析

根据西藏拉萨地区1990年、1995年和2000年3个时点的土地利用数据,应用马尔科夫过程模型分析了未来20年内拉萨地区的土地利用情景变化,并与90年代制定的拉萨地区土地利用规划面积进行了对比研究。研究结果:1)10年间,土地利用类型变化最广泛的是牧草地。变化方向主要由牧草地向耕地、园地、林地、居民点及水域转变,其中变成林地的面积最大,为2338.25hm²(占变化面积的94.093%);2)拉萨地区未来20年中土地利用类型发展趋势是耕地、牧草地、水域和未利用土地面积将进一步减少,林地、园地和居民点面积将进一步增加;3)土地利用规划面积与基于马尔科夫模型的土地利用变化情景分析结果比较吻合,马尔科夫过程模型对制定该区域土地利用规划具有重要的参考价值;4)由于土地利用变化是一个复杂的过程,不仅受到众多自然因素的影响,而且受到未来土地利用政策、社会经济发展、区域内大型工程项目及其他人类活动等不确定因素的影响,从而不同土地利用类型之间的转移概率会发生变化,使得基于马尔科夫过程模型预测的精度有一定的局限性。     

地理学多视角研究方法——Braess网络车流分配过程的理论分析与数值计算

对复杂的地理系统采用多种方法从不同的视角开展分析,可以降低错误结论的概率。本文以Braess交通网络为例,提出一个地理系统多视角分析的研究案例。首先借助La氏乘数法预测奇对称Braess网络的车流优化分配的结果。然后采用数值计算和模拟方法论证,在该网络中,车流会通过自组织过程自动向着优化分配的方向演化,并且利用Markov链预测各个阶段的车流分配数值。最后借助最大熵原理从理论上证明,上述最优化过程的本质是地理系统的熵最大化;运用对偶规划和对称思想揭示,熵最大化的实质是车流运行的耗时总量最小。不同的方法给出的结果殊途同归、互相印证。这一套研究方法可以推广到多维不对称的交通网络,进而推广应用于地理学其他方面的理论分析和应用研究。     

中老泰铁路建设对云南省及沿线国家空间联系格局的影响

中老泰铁路作为泛亚铁路东、中、西三线的中线,对中国与老挝、泰国的跨境合作以及助推“一带一路”倡议的“中国-中南半岛”经济走廊的建设起着重要作用。选取中国云南省和老挝、泰国作为研究区域,以非障区的区域开发为基础,并选取铁路沿线15个主要城市,基于城市流网络视角对比铁路建设前后区域可达性与城市流网络的变化,结果显示：铁路建成将提升障区可进入性与非障区可开发性,提高区域互联开发潜力,提升研究区内可达性。可达性呈现以铁路线路为轴向周围辐射递减的格局,但泰国西部及老挝东部自然障区可达性提升效果甚微;铁路将增强城市间联系强度,实现“树状-网状”的城市流网络形态高效重构,网络发育更加成熟稳固;铁路也使得网络结构扁平化、分散化,流网络重构过程中资源的分散与耗散并存,城市间联系效率下降。     

城市土地利用演变信息的数据挖掘——以上海市为例

城市土地利用变化 ,具有非线性特征 ,一般的数据挖掘方法基本上失效。本文研究了利用马尔可夫链和神经网络两种方法 ,基于地理信息系统、遥感图像、电子地图 ,预测了上海市中心城区、 2 0 0 2年和 2 0 0 5年的土地利用总量和土地利用类型结构的变化 ,从而研究了城市土地利用状况演变预测的地学数据挖掘技术。     

黑河流域水资源动态变化及其趋势的灰色Markov链预测

根据有关水文气象台站的观测数据,对黑河流域水资源时空分布特征及变化规律进行了分析,并基于GM(1,1)模型和Markov链原理,提出了一个用于黑河流域的水资源变化趋势预测的灰色离散随机过程模型。结果表明,从1944年有观测记录以来的近60a时间里,以黑河干流为代表的黑河流域天然径流经历了几个长度不等的丰水段与枯水段,而目前正处于1996年开始第5个丰水段的下降段的最低点或自2000年开始的第6个枯水段的起始点。未来几年里,黑河流域天然水资源的变化总体上将呈现一种偏枯或平水偏枯的状态,但径流偏枯的幅度将不会很大。     

基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测

由CO₂排放所引起的气候变化是当今社会所关注的热点话题,提高碳排放绩效是碳减排的重要途径。目前关于碳排放绩效的研究多从国家尺度和行业尺度进行探讨,由于能源消耗统计数据有限,缺乏城市尺度的研究。基于遥感模拟反演的1992—2013年中国各城市碳排放数据,采用超效率SBM模型对城市碳排放绩效进行测定,构建马尔可夫和空间马尔可夫概率转移矩阵,首次从城市尺度探讨了中国碳排放绩效的时空动态演变特征,并预测其长期演变的趋势。研究表明,中国城市碳排放绩效均值呈现波动中稳定上升的趋势,但整体仍处于较低的水平,未来城市碳排放绩效仍具有较大的提升空间,节能减排潜力大;全国城市碳排放绩效空间格局呈现“南高北低”特征,城市间碳排放绩效水平的差异性显著;空间马尔科夫概率转移矩阵结果显示,中国城市碳排放绩效类型转移具有稳定性,且存在“俱乐部收敛”现象,地理背景在中国城市碳排放绩效类型转移过程中发挥重要作用;从长期演变的趋势预测来看,中国碳排放绩效未来演变较为乐观,碳排放绩效随时间的推移而逐步提升,碳排放绩效分布呈现向高值集中的趋势。因此未来中国应继续加大节能减排力度以提高城市碳排放绩效,实现国家节能减排目标;同时不同地理背景的邻域城市之间应建立完善的经济合作联动机制,以此提升城市碳排放绩效水平并追求经济增长与节能减排之间协调发展,从而实现低碳城市建设和可持续发展。     

福贡县土地利用变化及演变趋势分析

以云南省福贡县1989、1999、2004年TM、ETM系列遥感数据作数据源,在RS和GIS技术支持下对研究区域1989-2004年的土地利用数量变化、变化速度、土地利用程度变化等方面进行分析;在此基础上,选用马尔柯夫模型对福贡县2010-2025年土地利用的动态演变趋势进行预测。结果表明,农业用地迅猛增加,而天然林、草甸草地、水体在缓慢减少;天然林的减少主要转化为农业用地、灌木林、草甸草地。经预测表明,福贡县2010-2025年建筑用地、冰川积雪、未利用土地的面积将基本不变;人工林面积将增加;农业用地、天然林、灌木林、草甸草地的面积将减少;水体面积变化较小。     

盐城湿地类型演化预测分析

以盐城湿地主要分布的三个县市:射阳、大丰和东台作为研究区,通过1988~2006年每隔6年的4期TM遥感影像,开展盐城湿地类型的动态变化分析,根据遥感影像解译结果,利用基于可拓物元模型的湿地演化元胞自动机(CA)模型和马尔可夫模型从空间和数据上进行湿地类型的变化预测。分析结果表明:(1)将基于可拓物元CA模型模拟预测结果与遥感分类结果进行逐个像元比较,计算得出相似程度达到 70%,因此基于可拓物元CA预测模型引入到湿地类型演化预测中是可行的。(2)基于可拓物元CA模型与马尔科夫模型的数值预测结果具有较高的相似度,表明研究区在2012年养殖场将成为盐城湿地最主要的湿地利用类型,预测结果分别达到了750.06km²和721.96km²,同时,耕地、居民用地、养殖场、米草面积总体呈增长趋势,滩地、芦苇、碱蓬和盐田呈剧烈减少趋势,米草逐渐占据优势物种,大面积的滩涂开发是盐城湿地类型变化最主要的原因。     

吉林西部土地荒漠化预测研究--以吉林省镇赉县为研究区

以吉林西部土地荒漠化典型区之一的镇赉县为研究对象，在GIS的支持下，根据研究区两个时期的遥感影象，解译出土地利用图和荒漠化土地分布图，通过叠加处理把荒漠化土地作为单独地类从其他土地利用类型中剔除，得到两期包括荒漠化地类的土地利用类型图，进一步叠加，得到监测期内镇赉县荒漠化土地相对于其他土地利用类型的时空变化。介绍了马尔柯夫荒漠化预测模型的建立过程，并应用马尔柯夫模型预测和分析了镇赉县土地荒漠化的演化趋势，结果表明：如不采取有效措施，镇赉县荒漠化将继续发展，荒漠化土地将逐步蚕食掉现有的草地、林地和耕地，2050年，荒漠化土地将占研究区的32．49％。     

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.     

Anomalous behavior detection in single-trajectory data

This paper presents an original approach to dynamic anomalous behavior detection in individual trajectory using a recursive Bayesian filter. The anomalous pattern detection is of great interest for navigation, driver assistance systems, surveillance as well as crisis management. In this work, we focus on the GPS trajectories of automobiles finding where the driver’s behavior shows anomalies. Such anomalous behaviors can happen in many cases, especially when the driver encounters orientation problems, i.e., taking a wrong turn, performing a detour, or losing the way. First, three high-level features, i.e., turns and their density, detour factor, and route repetition are extracted from the given trajectory geometry, for which a long-term perspective is required to observe data sequences of a significant length instead of individual time stamps. We therefore employ high-order Markov chains with a ‘dynamic memory’ to model the trajectory integrating these long-term features. The Markov model is processed by a proposed recursive Bayesian filter to infer an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The filter performs unsupervised detection in single trajectories based on local features only. No training process is required to characterize the anomalous behaviors. By analyzing the detection results of individual trajectories, collective behaviors can be derived indicating traffic issues such as congestions and turn restrictions. Experiments are performed on volunteered geographic information (VGI) data, self-acquired trajectories, and open trajectory datasets to demonstrate the potential of the proposed approach.     

基于GA复合模型的城市空间演变研究

介绍了元胞自动机（CA）模型、马尔柯夫（MARKOV）和层次分析（AHP）的概念和原理,在分析城市空间演化过程和模型特点的基础上,对标准CA模型进行扩展,构造出CA-MARKOV-AHP复合模型.在GIS和基础地理信息数据库的支持下,对广东江门城市空间的演变进行模拟和预测.     

Some further clarification on Markov chain random fields and transiograms

The Markov chain random field (MCRF) theory and the transiogram spatial measure were proposed several years ago. Basic sequential simulation algorithms based on simple MCRF models such as the Markov chain sequential simulation algorithm and the Markov chain sequential co-simulation algorithm have been developed and used in a series of application studies. However, misunderstanding of these two ideas and the geostatistical approach built on them arose recently among some researchers in geostatistics. The purpose of this article is to further clarify some issues related to these two ideas, so as to avoid further misunderstanding. For those issues already clarified, trivial, or obviously irrelevant, we do not talk about them here.     

基于地学信息图谱的土地利用动态变化研究——以山东省龙口市为例

阐述了地学信息图谱的内涵及其概念模式.选择山东省龙口市为例,在遥感和地理信息系统技术支持下,利用2002年数字遥感图像和1996年土地利用图及其自然和社会经济统计数据,建立了龙口市土地利用动态变化信息图谱,并根据图谱从空间和时间两方面分析龙口市土地利用动态演变过程,最后结合马尔科夫模型对其未来的演变趋势进行动态预测.研究表明:自1996年以来,龙口市土地利用发生了大幅度变化,耕地面积减少5 383.48 hm2,园地和建设用地面积各增加33 729.62 hm2和2319.91 hm2;同时土地利用结构发生明显变化,主要表现在耕地和园地之间的转化,耕地和园地向建设用地的转化.预测表明:在未来的几十年内,耕地面积仍将继续减少,建设用地面积还会逐年增加,园地仍为该市的主要用地类型.