摘要
随着海量、多源的高分辨率遥感数据的获取,耗时较多、效率低下的传统处理方式已经不能满足用户需求。针对上述问题,提出了一种基于云计算的高分遥感数据处理框架,利用Hadoop技术设计和改进了Meanshift图像边缘分割算法,并在Hadoop环境下进行了仿真实验。实验结果表明,在Hadoop环境下的高分辨率卫星图像数据处理速度有了明显的改善。
As vast and multi-source high-resolution remote sensing data is acquired, time consuming and low efficiency of traditional processing way have already can not meet the needs of users. According to the above problem, this paper puts forward a high score of remote sensing data processing based on cloud computing framework, designs and improves the Meanshift algorithms of image edge segmentation by using Hadoop technology, and has carried on the simulation experiment in the Hadoop environment. Experimental results show that high resolution satellite image data processing speed under the Hadoop environment is improved obviously.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第11期167-171,共5页
Computer Engineering and Applications
基金
国家科技支撑计划课题(No.2012BAH09B02)
长沙市重点科技计划项目(No.K1204006-11-1)
关键词
高分辨率遥感数据
云计算
HADOOP
边缘分割
high resolution remote sensing data
cloud computing
Hadoop
edge segmentation
