Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

矿床成矿系列——五论矿床的成矿系列问题

矿床的成矿系列(简称成矿系列)是矿床学领域的一个理论性概念,由五级序次组成。矿床成矿系列是成矿系列的第二序次,是成矿系列的核心部分。矿床成矿系列的划分,以岩浆、沉积、变质、表生和流体(非岩浆-非变质成因流体)5种成矿作用为基本原则,以成矿地质环境为基础,结合成矿的时段与形成的矿床组合进行划分。本文对矿床成矿系列时空范围、时空组成结构、矿化强度与演化、成矿区带内不同矿床成矿系列之间的演化、叠加和复合作用及对指导找矿的意义进行了论述。     

基于面向对象分类的稀土开采区遥感信息提取方法研究

离子吸附型稀土矿是我国宝贵的矿产资源,运用遥感影像分类技术提取稀土开采区可以准确地实现对稀土开采状况的监测,但仅利用光谱信息往往难以保证分类精度。本文以江西寻乌稀土矿区为研究区,以IKONOS影像为数据源,应用面向对象分类方法提取了稀土开采区的遥感信息。针对稀土开采区的分布特点,选择基于边缘的分割算法进行影像分割;结合地形信息、光谱信息及几何信息建立规则集,进行特征提取;最后采用隶属度函数法实现面向对象分类,并与传统的光谱角填图分类进行对比分析。研究结果表明,面向对象分类法提取稀土开采区的总体精度为92.49%,Kappa系数为0.857 6,与传统监督分类方法相比有了很大的提高。     

Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges

Bracken fern is one of the major invasive plants distributed all over the world currently threatening socio-economic and ecological systems due to its ability to swiftly colonize landscapes. The study aimed at reviewing the progress and challenges in detecting and mapping of bracken fern weeds using different remote sensing techniques. Evidence from literature have revealed that traditional methods such as field surveys and modelling have been insufficient in detecting and mapping the spatial distribution of bracken fern at a regional scale. The applications of medium spatial resolution sensors have been constrained by their limited spatial, spectral and radiometric capabilities in detecting and mapping bracken fern. On the other hand, the availability of most of these data-sets free of charge, large swath width and their high temporal resolution have significantly improved remote sensing of bracken fern. The use of commercial satellite data with high resolution have also proven useful in providing fine spectral and spatial resolution capabilities that are primarily essential to offer precise and reliable data on the spatial distribution of invasive species. However, the application of these data-sets is largely restricted to smaller areas, due to high costs and huge data volumes. Studies on bracken fern classification have extensively adopted traditional classification methods such as supervised maximum likelihood classifier. In studies where traditional methods performed poorly, the combination of soft classifiers such as super resolution analysis and traditional methods of classification have shown an improvement in bracken fern classification. Finally, since high spatial resolution sensors are expensive to acquire and have small swath width, the current study recommends that future research can also consider investigating the utility of the freely available recently launched sensors with a global footprint that has the potential to provide invaluable information for repeated measurement of invasive species over time and space.     

Object- and pixel-based classifications of macroalgae farming area with high spatial resolution imagery

Macroalgae plays an important role in coastal ecosystems. The accurate delineation of macroalgae areas is important for environmental management. This study compared the pixel- and object-based methods using Gaofen satellite no. 2 image to explore an efficient classification approach. Expert system rules and nearest neighbour classifier were adopted for object-based classification, whereas maximum likelihood classifier was implemented in the pixel-based approach. Normalized difference vegetation index, normalized difference water index, mean value of the blue band and geometric characteristics were selected as features to distinguish macroalgae farms by considering the spectral and spatial characteristics. Results show that the object-based method achieved a higher overall accuracy and kappa coefficient than the pixel-based method. Moreover, the object-based approach displayed superiority in identifying Porphyra class. These findings suggest that the object-based method can delineate macroalgae farming areas efficiently and be applied in the future to monitor the macroalgae farms with high spatial resolution imagery.     

地下管线普查产品的检验

随着城市地下管线在城市管理、规划及建设发展过程中重要性的逐渐体现,越来越多的城市开展了地下管线普查,但其质量检验与质量评定方法却相对滞后,本文结合实例阐述了地下管线普查产品的检验及质量评定方法,供大家参考。     

改进的P-SVM支持向量机与遥感数据分类

本文介绍了将P-SVM算法引入多光谱/高分辨率遥感数据的分类, 并且展示了卫星ASTER和航空ADS40数字影像分类的技术过程和结果验证。结果表明：P-SVM方法的分类精度不低于SVM, 并减少了时耗。     

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.     

台湾恒春半岛的隆起珊瑚礁台地

台湾本岛南端的恒春半岛,出露隆起珊瑚礁石灰岩和现代珊瑚礁海岸。隆起珊瑚礁石灰岩在地貌上的表现是多阶的台地。最高的公园面可达海拔300m,依次向下,是海拔约100～160m的笼仔埔面,海拔70～80m的埔顶面以及海拔10～20m的垦丁面。在恒春半岛的西侧,珊瑚礁台地面已经倾斜,因此高度的表现不同。ESR及14C定年表明,抬升的更新世珊瑚礁台地形成于0.5Ma B.P.前后及0.13Ma B.P.两时期;海拔10～20m的全新世珊瑚礁台地约形成于6 000a B.P.的时期。这些证据显示了地质时期里全球变迁的蛛丝马迹。     

东山ZK51孔第四纪地层划分与闽南沿海部分地区地层划分对比

福建沿海全新世地层 ,主要分布在沿海地区 级阶地上。裸露地表一般为晚全新世沉积、洪积、冲积、冲洪积层 ;中、早全新世地层多被晚全新世地层覆盖 ,很少裸露地表。根据东山 ZK51孔揭穿的全新世地层中鉴定的微体古生物、孢子花粉、14 C同位素年龄测试结果 ,结合宏观资料进行分析对比 ,将全新世地层划为晚、中、早全新世。晚、中全新世地层的分界经 14 C同位素年龄测试 ,距今 2 56 0± 90 a;中、早全新世地层分界经 14 C同位素年龄测试 ,距今 896 0± 2 4 0 a。这些测试结果 ,与目前国内外对全新世地层划分十分接近。进将中、早全新世地层均划分为 a、b两段。中全新世地层以小泡虫、泡抱球虫组合和喜暖的底有孔虫组合为特征 ,而且经 14 C同位素年龄测试 ,距今 4 310± 110 a,前者为 Q2 ah 、后者为 Q2 bh ;早全新世以冷水面颊虫组合和小泡虫组合为特征 ,将前者划为 Q1ah ,后者划为 Q1bh 。可与欧洲前北方期、北方期和大西洋期、亚北方期进行对比。