基于ArcGIS Server以及Flex的富客户端技术,以厦门理工学院为例,设计开发了虚拟校园系统.该系统采用B/S架构,利用Papervision3D(PV3D)引擎实现校园全景图像的渲染;使用Flex的富客户端交互技术实现校园全景的漫游,利用ArcGIS Flex for AP...基于ArcGIS Server以及Flex的富客户端技术,以厦门理工学院为例,设计开发了虚拟校园系统.该系统采用B/S架构,利用Papervision3D(PV3D)引擎实现校园全景图像的渲染;使用Flex的富客户端交互技术实现校园全景的漫游,利用ArcGIS Flex for API调用ArcGIS Server发布的相关地图服务以及GIS工具服务实现地图展示、地图查询、距离测量等空间分析功能.该系统结合了全景数据与二维地图数据,实现了两类数据的联动.展开更多
Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVlSAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component ana...Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVlSAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis （PCA） was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the W and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st compo- nent, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier.展开更多
基金Supported by the Program for New Century Excellent Talents in University (NCET-05-0573)Fujian Science and Technology Project (No2006I0018)the Science Project of the Education Department of Fujian Province(No 2006F5022)
文摘Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVlSAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis （PCA） was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the W and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st compo- nent, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier.
