基于C4.5算法的道路网网格模式识别

道路网模式的识别对于地图综合、数据匹配和空间分析具有重要意义。网格模式是道路网中的典型模式之一。本文提出一种基于C4.5算法的网格模式识别方法。该方法以道路网中的网眼多边形为基本单元,根据上下文关系将其标识为属于网格模式和不属于网格模式两类。首先采用形状参量和关系参量描述网眼多边形,然后,基于决策树C4.5算法分别对5维参量和3维参量构造分类器,运用10折交叉验证获得具有说服力的结果,其Kappa值分别为0.63和0.66,正确率分别为81.7％和82.9％,置信度90％的置信区间分别为[0.785, 0.846]和[0.797, 0.857]。在新数据上进行了识别效果的验证,结果表明该分类器可用于网格模式的识别。研究试图将传统模式识别和数据挖掘的理论方法应用于空间问题的解答中。     

Matching authority and VGI road networks using an extended node-based matching algorithm

The amount of volunteered geographic information (VGI) has increased over the past decade, and several studies have been conducted to evaluate the quality of VGI data. In this study, we evaluate the completeness of the road network in the VGI data set OpenStreetMap (OSM). The evaluation is based on an accurate and efficient network-matching algorithm. The study begins with a comparison of the two main strategies for network matching: segment-based and node-based matching. The comparison shows that the result quality is comparable for the two strategies, but the node-based result is considerably more computationally efficient. Therefore, we improve the accuracy of node-based algorithm by handling topological relationships and detecting patterns of complicated network components. Finally, we conduct a case study on the extended node-based algorithm in which we match OSM to the Swedish National Road Database (NVDB) in Scania, Sweden. The case study reveals that OSM has a completeness of 87% in the urban areas and 69% in the rural areas of Scania. The accuracy of the matching process is approximately 95%. The conclusion is that the extended node-based algorithm is sufficiently accurate and efficient for conducting surveys of the quality of OSM and other VGI road data sets in large geographic regions.     

面向对象分类的决策树方法探讨——以Landsat-8OLI为例

针对目前遥感影像分类中面向对象和决策树相结合的研究较少的情况,该文提出基于C5.0决策树的面向对象分类方法,并以广州市从化区进行实证研究。基于Landsat-8OLI影像数据,采用面向对象分类对影像进行多尺度分割,提取出影像对象的光谱、纹理特征以及影像对象相对应的DEM信息;然后利用C5.0决策树根据特征信息来挖掘分类规则;最后根据规则对分割后影像进行分类。结果表明,基于C5.0决策树的面向对象影像分类精度高、效果好,总体精度和Kappa系数分别为89.75%和87.5%。该方法可准确、快速地提取土地利用/覆被信息。     

Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.     

平行路段模式识别与简化初探

道路网模式中的平行路段作为一种局部模式,其识别与简化是地图综合研究的重要内容。本文在分析平行路段产生的原因和常见形态后,给出一种利于简化的平行路段分类方法,并提出基于平头缓冲区搜寻备选平行路段进而利用平行性测度判断路段是否平行的方法。在此基础上,针对3类平行路段分别提出对应的几何化简与连通关系简化方法。实验结果表明,该识别与简化方法能够对路网模式中的平行路段进行有效地综合,具有一定的实用价值,对进一步研究具有参考价值。     

运用图论进行道路网网格模式提取

提出了一种基于图论的网格模式提取方法。该方法根据道路之间的关系生成关系图,运用交、联、提取连通分量和极大完全子图等图论算子完成模式的提取。实验结果表明,该方法能有效地进行网格模式的提取。     

Maintaining accurate,current, rural road network data: An extraction and updating routine using RapidEye,participatory GIS and deep learning

Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained.     

云阳宝塔滑坡监测的变形模式识别与分析

根据变形观测的位移矢量,识别变形体及其运动模式,是变形分析的一项重要内容。以往的作法是根据矢量作图、人工判断变形块体,然后选择适当的模式进行拟合。本文提出了一种在点位邻接拓扑关系加其他先验信息构成约束的识别思想及相应算法,从而可实现变形模式的自动识别。最后的算例说明了该算法的实现过程,有效性及潜在意义。     

离散点集Delaunay三角网生成算法改进与软件开发

改进了三角形生长算法，在原算法的基础上，用均匀格网来划分离散点集，建立点集的栅格索引,快速生成初始三角形，再进行扩展；引进了封闭点的概念，在扩展过程中动态删除封闭点，加快了查找点的搜索过程；软件开发中采用VC的MPC类Carry和CList来管理数据，创建了CBuildtin和Craster两个类来生成三角网和栅格索引，提高了程序的可移植性。     

FY-2C云分类资料估算日照百分率

日照百分率是地球辐射研究、太阳能资源评估等工作中使用的一个重要指标量。为探讨FY-2C静止气象卫星资料估算日照百分率的可能性,以江西省为例,采用了FY-2C静止气象卫星2007年整年白天逐小时云检测资料和同期江西省87个国家气象站逐小时日照时数资料,建立了北京时间8:00-17:00逐时多元线性回归和权重系数两种估算模型,估算得到了覆盖江西省范围的5 km空间分辨率的逐时日照百分率格点资料。另外,用反距离加权(IDW)和克里金法(Kriging)模型对全省8:00-17:00逐时的日照百分率进行5 km分辨率空间插值,以进行多种方法对比分析。将各模型的估算结果与未参与模型构建的17站实测资料进行对比分析后发现,基于FY-2C云检测资料的两种估算模型的平均绝对误差(MAE)和相对误差均比IDW和Kriging的小50%以上,且其均方根误差(RMSIE)均明显小于后两者,误差离散性亦更小。基于FY-2C云检测资料的日照百分率的两个估算模型中多元回归模型的平均绝对误差(MAE)为3.40%,平均相对误差为8.62%,略小于权重系数模型的3.47%和8.75%,两者的均方根误差(RMSIE)则相差无几,误差空间分布也较为一致;各时次两种模型的估算值多大于实测值,仅17:00的估算结果小于实测值,8:00和17:00的MAE、RMSIE、相对误差均明显大于其他时次。对试验结果进行综合分析得出结论:基于FY-2C云检测资料的两种日照百分率估算模型均明显优于传统的IDW和Kriging方法;就2007年资料而言,多元回归模型优于权重系数模型。     

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.     

模糊模式识别在提高地图矢量化精度中的应用研究

在GIS地图矢量化中,由于原始数据的获取,软件算法精度等原因使得矢量图形存在误差,本文阐述了模糊模式识别方法的原理,探讨了利用模糊模式识别对一些常用图形(矩形、圆、三角形等)进行矫正的方法,并应用于对一些具有模糊特征的地物边界进行判别。     

Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands,India, using IRS LISS III satellite images

A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.     

Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm

The objective of this paper is to demonstrate a new method to map the distributions of C₃ and C₄ grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C₃ and C₄ grasses in our study area such as elevation, soil pH, soil organic matter and soil texture.     

中国东南沿海地区大气反常结构遥感初探

对流层大气作为电波传播的主要媒介,其大气剖面参数直接影响无线电系统的作用距离和导航、定位精度,因此,研究大气剖面的微波遥感技术,尤其是大气反常结构,对分析评估无线电信息系统在复杂大气环境条件下工作性能和提供电波折射修正精度等具有重要的应用价值。中国东南沿海地区一直是研究的热点区域,本文选取青岛、射阳、厦门、海口4个站的气象探空仪观测数据,统计了这些地区大气波导的平均高度和厚度,对采用地基多通道微波辐射计的天顶和扫角亮温遥感其参数的算法进行了初步探索,提出了改进的组合神经网络算法来判别并遥感大气波导。同时,使用多年的探空数据对该算法进行了仿真验证,并用青岛气象站的实测数据验证了该算法的精度。结果表明该算法可用于遥感大气反常结构,并且与以往的算法相比更加实用、有效,可为雷达、通信、导航等系统设备提供环境信息保障。     

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi,Vietnam

ABSTRACT

This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 ? 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi’s weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.     

嵌入式GIS中最优路径规划算法研究与实现

针对现有大区域范围路径规划算法存在的一些问题,提出一种多比例尺最优路径规划算法。该算法在进行路径规划时,在小区域范围内根据道路的属性信息对路网进行分层,在大区域范围内根据多比例尺信息对路网进行分级,在每级或每层内采用基于弧段的改进型A*算法。从实例分析可以看出,该算法能较好满足车载导航系统的需求。     

The decision of the optimal parameters in Markov random fields of images by genetic algorithm

This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters. Focusing on the shortcomings in present methods, a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field. The detailed procedure is discussed. On the basis of the parameters solved by genetic algorithms, some experiments on classification of aerial images are given. Experimental results show that the proposed method is effective and the classification results are satisfactory.     

Remote sensing of urban growth and landscape pattern changes in response to the expansion of Chongming Island in Shanghai,China

Chongming Island in China is currently undergoing a rapid urbanization and an increasing environmental pressure due to its fast-paced social economic development. Owing to natural silts deposit phenomenon as well as artificial land reclamation practices, the Island is also experiencing an expansion phenomenon. The synergy between these natural and artificial phenomena results in a rapidly changing landscape on Chongming Island. Consequently, the tools and methods for a rapid and cost-effective detection and assessment of related issues are urgently needed to ensure a harmonious and sustainable development of the Island. We herein investigate the urban growth and the landscape pattern change in relation to the Island’s expansion phenomenon and the associated complexity of land use/cover change. Our investigation is based on a time series of Landsat satellite images spanning the past 34 years. The methodological approach adopted in the present study combines vegetation indices, images textural features and social statistics data in an object-oriented classification framework. With Chongming Island expanding by an annual rate of 0.9% between 1979 and 2013, we found that the proportion of vegetation area to the total area decreased from 71 to 45%, whereas the proportion of built-up area to the total area increased from 5 to 19.9%. The urban area expanded about six times from 1979 to 2013, and during the same period, the Island’s population did not change significantly. The urban spatial expansion of Chongming Island caused distinct expansion intensity index for each intervals, and significant fragmentation and diversity in the landscape pattern between 1979 and 2013. It was also found that the rapid urbanization process took place at the expense of landscape pattern changes at any time within the study period. This is a strong indication that besides the natural geographic element, economic development and policy orientation were the dominant driving factors. If the current rate of urban expansion is to be maintained and the vegetation cover is to keep decreasing at an annual rate of 0.1% (period 1979–2013), their combined effects would profoundly alter the ecological environment in the long term. These findings provide a basic objective and scientific information for knowledgeable decision-making and policy formulation regarding regional planning and management to ensure harmonious transition of Chongming towards ecologically oriented development.     

The decision of the optimal parameters in Markov random fields of images by genetic algorithm

This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experim ents on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.