基于SVM的资源三号测绘卫星影像分类

以江苏省宜兴市为研究区,利用支持向量机(SVM)方法对资源三号测绘卫星影像进行了分类,其总分类精度为97.76%,Kappa精度为0.968 7。为了评价算法的适用性,同时应用最大似然法与最小距离法对同一影像进行分类测试,支持向量机分类法精度高于其他2种方法,可以满足土地覆盖分类调查需求。     

A Review of: Environmental Modelling with GIS.

Cartographic modelling operations provide powerful tools for analysing and manipulating geographic data in the raster data model. This research extends these operations to the vector data model. It first discusses how the spatial scopes of analysis can be defined for point, line, and polygon features analogous to the raster cell. Then it introduces the local, focal, and zonal operations available for vector features, followed by providing a prototype syntax that might guide the implementation of these operations. Through example applications, this research also demonstrates the usefulness of these operations by comparing them with traditional vector spatial analysis.     

A proposed framework for feature‐level geospatial data sharing: a case study for transportation network data

Current data sharing in the Internet environment is supported using metadata at the file level. This approach has three fundamental shortcomings. First, sharing data from different sources with different semantics, data models, and acquisition methods usually requires data conversion and/or integration like data conflation. This can be tedious and error‐prone. Second, data updated from one source cannot be automatically propagated to other related data or applications. Finally, data sharing at the file level makes it difficult to provide feature‐level data for searching, accessing, and exchanging in real time over the Internet. This paper addresses these three issues by proposing a standards‐based framework for sharing geospatial data in the transportation application domain. The proposed framework uses a standard data model—geospatial data model proposed by the Geospatial One‐Stop initiative to harmonize the semantics and data models without the use of data integration methods. It uses Geography Markup Language (GML) for geospatial data coding and feature relationship, which provides a basis to propagate the data update from one source to related other sources and applications, and to search and extract data at the feature level. The framework uses the Web Feature Service (WFS) to search, access and extract data at the feature level from distributed sources. Finally, the Scalable Vector Graphics (SVG) standard was used for data display on the Web browser. Two transportation network datasets are used in the prototype case study to implement the proposed framework. The prototype allows the user to access and extract data at the feature level on the Web from distributed sources without downloading the full data file. It shows that the proposed standards‐based feature‐level data‐sharing system is capable of sharing data without data conflation, accessing, and exchanging data in real time at the feature level. The prototype also shows that changes in one database can be automatically reflected or propagated in another related database without data downloading.     

Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs

This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd.     

Replication of a terrain stability mapping using an Artificial Neural Network

Subjective geomorphic mapping is a method commonly used for landslide hazard zonation. This method relies heavily on the skills and experience of the mapper, and therefore, its major drawbacks are the high costs and lack of consistency between products generated by different terrain mappers. In this study a method for cost-effective and consistent replication of subjective geomorphic mappings is demonstrated, by using a type of Artificial Neural Network named Learning Vector Quantization. This paper presents a study conducted in the Canadian province of British Columbia employing a high-quality data set. By utilizing Learning Vector Quantization, stable and unstable terrains were delineated with a similarity of approximately 91%, compared to the mapping produced by terrain specialists. Also, in this process, slope, elevation, aspect, and existing geomorphic processes were identified as the terrain attributes that contributed most to the quality of the mapping.     

基于SVG的WebGIS

网络技术的飞速发展不但在于技术的深化．更在于新技术元素的创立。SVG作为网络图形的新家族成员,一改过去网络图形只重形式,忽略内容的缺点,渐渐显示其独特且强大之处,也为GIS提供了一个方便快捷的网络平台。本文探究基础地理信息系统功能在SVG中的实现技术。     

秸秆焚烧导致湖北中东部一次严重霾天气过程的分析

利用地面气象要素、火点信息及污染物资料,研究了2014年6月12~13日湖北省中东部地区一次重度霾天气的成因及污染特征。结果表明:导致此次霾天气的主要原因是安徽省北部大面积秸秆焚烧所形成污染气团受偏东北气流输送的影响,12日在湖北中东部形成了两条"带状"的能见度低值区,最低能见度仅为2.1 km。秸秆焚烧污染物输送气流由北向南影响湖北,主要作用于孝感—武汉—咸宁一带,3个地区细颗粒物(PM2.5)峰值浓度均超过了600μg/m3,且武汉和孝感的PM2.5与PM10质量浓度比值在12日增加到0.76和0.77,并出现了0.96和0.93的最大值,随着污染气团的传输,其中PM2.5所占比例会出现明显下降。SO2质量浓度的变化特征不显著,NO2质量浓度在污染物质量浓度达到峰值前1~3 h达到峰值,而CO是秸秆焚烧产生的主要污染气体,其质量浓度变化与PM2.5和PM10呈正相关关系,相关系数分别为0.66和0.67。风矢量和分析表明:6月12日湖北省中东部存在明显的东北来向气流输送,污染物的输送是该时段霾天气发生的主要影响因子,而6月13日湖北省东北边界处的输送气流已经明显减弱消失,东南部风矢量和异常偏小导致的污染物堆积是该地区污染持续的主要原因。     

天空云量预报及支持向量机和神经网络方法比较研究

使用支持向量机和人工神经网络两种方法,分别建立了天空云量的预报模型。利用2001年5月1日～2004年12月31日的武汉市地面、高空观测值及欧洲中心的24小时预报场等资料,通过按不同比例随机抽取样本进行交叉验证的方法,分析了SVM和ANN模型的预报能力和鲁棒性;然后再用全部样本资料建立预报模型,来预报2005年1月1日～5月31日武汉市天空云量。交叉验证和实例预报的结果显示：虽然SVM和ANN模型都表现了较好的预报能力,但SVM的预报能力高于ANN方法,且在计算速度上有ANN无法比拟的优势。     

遥感影像屏幕数字化高效方法研究--显著地物的矢量跟踪方法

主要针对遥感影像屏幕数字化效率较低问题,提出了显著地物矢量跟踪方法：包括显著地物的分析、遥感影像数据的预处理、显著地物信息的人机交互提取、显著地物的矢量跟踪等步骤,并利用辽宁省东部新宾县ＴＭ遥感影像对此方法进行了应用与验证。     

多核学习算法及其在高光谱图像分类中的应用研究进展

多核学习算法在高光谱图像分类领域占据着十分重要的地位。与灰度图像、全色图像和多光谱图像等相比,高光谱图像因具有很强的分类识别能力等多方面优势而被广泛应用。为进一步提高高光谱图像的分类精度,促进多核学习算法在高光谱图像分类中的应用,本文对多核学习算法及其在高光谱图像分类中的应用进行了总结。首先在回顾核方法的基础上阐述了多核学习框架,其次对多核学习核函数组合方法进行综述,随后根据求解多核学习组合系数方法的不同将多核学习分为两类：固定规则的多核学习算法和基于优化的多核学习算法,并对两类多核学习算法在高光谱图像分类中的应用进行综述,总结各类算法在高光谱图像分类的应用进展。同时,为了便于研究者对多核学习算法及其在高光谱图像分类问题中的应用研究,本文对常用核函数和高光谱图像数据集进行了整理归纳。最后,讨论了多核学习算法在高光谱图像分类研究方面的不足,并对未来研究方向进行了展望,以期为该领域的研究和应用提供参考。