The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessing services to geospatial users.This paper provides a comparative analysis of geoprocessing in Cloud Computing platformsMicrosoft Windows Azure and Google App Engine.The analysis compares differences in the data storage,architecture model,and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms;emphasizes the importance of virtualization;recommends applications of hybrid geoprocessing Clouds,and suggests an interoperable solution on geoprocessing Cloud services.The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern,once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies.The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms.The tested services are developed using geoprocessing algorithms from different vendors,GeoSurf and Java Topology Suite.The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.
A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for building a unified and interoperable geospatial cyberinfrastructure.In this paper,we propose the Geospatial Service Web(GSW)to underpin the development of a future geospatial cyberinfrastructure.The GSW excels over the traditional spatial data infrastructure by providing a highly intelligent geospatial middleware to integrate various geospatial resources through the Internet based on interoperable Web service technologies.The development of the GSW focuses on the establishment of a platform where data,information,and knowledge can be shared and exchanged in an interoperable manner.Theoretically,we describe the conceptual framework and research challenges for GSW,and then introduce our recent research toward building a GSW.A research agenda for building a GSW is also presented in the paper.
提出一种基于局部判别正切空间排列(local discriminative tangent space alignment,LDTSA)的高光谱影像降维方法。LDTSA源于局部正切空间排列(LTSA)中的排列机制,在一个局域块内利用线性局部正切平面对类内样本的流形结构建模,同时还考虑到类间判别信息以最大化判别边界。利用多幅高光谱数据进行降维和分类试验。结果表明,LDTSA主要有三个优点:①在小样本问题上性能稳定;②在降维过程中保持类别间的判别信息;③有效挖掘数据集的几何流形结构。
