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基于OpenMP的遥感影像并行ISODATA聚类研究

刘扬1a,王鹏1a,2,杨瑞1a,左宪禹1b,张周威2,吴晓洋1a,2,渠涧涛1a,2   

  1. (1.河南大学 a.空间信息处理实验室; b.数据与知识工程研究所,河南 开封 475004; 2.中国科学院 遥感与数字地球研究所,北京 100101)
  • 收稿日期:2015-05-27 出版日期:2016-07-15 发布日期:2016-07-15
  • 作者简介:刘扬(1971-),男,副教授、博士,主研方向为媒体神经认知计算、图形图像处理;王鹏、杨瑞,硕士研究生;左宪禹,讲师、博士;张周威,博士;吴晓洋、渠涧涛,硕士研究生。
  • 基金资助:
    国家自然科学基金资助项目(61202098);中国博士后科学基金资助面上项目(2014M552001);河南省教育厅科学技术研究基金资助重点项目(13A520071);高分辨率对地观测系统重大专项基金资助项目(Y4D0100038)。

Research on Parallel ISODATA Clustering for Remote Sensing Image Based on OpenMP

LIU Yang1a,WANG Peng  1a,2,YANG Rui 1a,ZUO Xianyu 1b,ZHANG Zhouwei 2,WU Xiaoyang 1a,2,QU Jiantao 1a,2   

  1. (1a.Laboratory of Spatial Information Processing;1b.Institute of Data and Knowledge Engineering,Henan University,Kaifeng,Henan 475004,China; 2.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China)
  • Received:2015-05-27 Online:2016-07-15 Published:2016-07-15

摘要: 针对传统影像分类算法执行效率较低,无法满足海量高分辨率遥感数据实时处理需求的问题,对资源三号卫星专题产品中遥感影像的迭代自组织数据分析算法进行分析与研究,设计一种基于OpenMP的并行ISODATA聚类算法(PIsodataOmp)。采用OpenMP技术优化ISODATA算法中的样本点聚类、聚类样本中心标准差计算,实现基于共享内存的单机多核并行化处理。实验结果表明,PIsodataOmp算法能在保证分类精度不变的情况下,明显提高资源三号卫星影像数据的处理速度。

关键词: 并行聚类, 迭代自组织数据分析算法, OpenMP技术, 遥感影像分类, 多核处理

Abstract: The traditional image classification algorithm suffers from low efficiency and is unable to meet the requirement of mass and real-time processing of high resolution remote sensing data.Based on the analysis and research of the Iterative Self-organizing Data Analysis Techniques Algorithm(ISODATA) for remote sensing images of ZY-3 satellite thematic production, this paper designs a Parallel ISODATA Based on OpenMP(PIsodataOmp).The OpenMP technology is used for the clustering of sample points and the calculation of the standard deviation of the clustering center, realizing parallel processing on a single multi-core computer based on the shared memory.Experimental results show that, when using the PIso data Omp classification to process the image data of ZY-3 satellite, the speed of classification is improved significantly while ensuring the accuracy of classification.

Key words: parallel clustering, Iterative Self-organizing Data Analysis Technique Algorithm(ISODATA), OpenMP technology, remote sensing image classification, multi-core processing

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