遥感大数据实时渲染与交互可视化研究

doi:10.3724/SP.J.1047.2016.00664

Abstract

Abstract:

Data visualization is an important service in remote sensing applications. To address the problems that it is difficult for the static pre-built map tile service to meet the requirements of professional data view, map configuration, spatial analysis and other applications, this paper presented a solution architecture for the real-time rendering and interactive visualization of remote sensing big data. Firstly, on the rendering nodes, a rendering-tile structure for image was constructed to improve the reading speed of remote sensing images. Secondly, on the visualization servers, a data-computing load balancing strategy was proposed to optimize the rendering efficiency of map tiles. Thirdly, a set of service interfaces for the interactive visualization was designed for the front ends of services. Compared with the traditional technology, this solution can not only achieve the real-time rendering and the interactive visualization of remote sensing data, but also obtain an equivalent service performance to the pre-built tile map service. Finally, based on the above solutions, an interactive visualization prototype system of remote sensing data was developed and was applied into the demonstrations of the real-time viewing of remote sensing images, the visualized computing and the visualized analysis.

Key words: RS big data, WMTS, real-time rendering, interactive visualization

ZHOU Ya'nan,ZHAO Wei,FAN Ya'nan. Real-time Rendering and Interactive Visualization of Remote Sensing Big Data[J].Journal of Geo-information Science, 2016, 18(5): 664-672.DOI:10.3724/SP.J.1047.2016.00664

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名称	RESTful接口	描述
新建空白地图	POST：http://mapserver/rastermap/	创建一个新的地图,并返回地图的唯一标示字符串
配置地图	PUT：http://mapserver/rastermap/{mapid}	将交互前端生成的地图配置集更新到服务后台实现地图配置
删除地图	DEL：http://mapserver/rastermap/{mapid}	删除已有的影像地图
缓存地图瓦片	POST：http://mapserver/tilecache/{mapid}	生成地图的瓦片缓存,以供静态地图服务用
载入地图	GET：http://mapserver/rastermap/{mapid}	载入已有的地图配置

Real-time Rendering and Interactive Visualization of Remote Sensing Big Data

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