Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1296-1301.DOI: 10.11772/j.issn.1001-9081.2015.05.1296

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Utilizing multi-core CPU to accelerate remote sensing image classification based on K-means algorithm

WU Jiexuan, CHEN Zhenjie, ZHANG Yunqian, PIAN Yuzhe, ZHOU Chen   

  1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology (Nanjing University), Nanjing Jiangsu 210023, China
  • Received:2014-12-15 Revised:2015-01-11 Online:2015-05-10 Published:2015-05-14


吴洁璇, 陈振杰, 张云倩, 骈宇哲, 周琛   

  1. 江苏省地理信息技术重点实验室(南京大学), 南京 210023
  • 通讯作者: 陈振杰
  • 作者简介:吴洁璇(1991-),女,浙江台州人,硕士研究生,主要研究方向:高性能地理计算、数据挖掘; 陈振杰(1974-),男,陕西宝鸡人,副教授,博士,主要研究方向:GIS算法、高性能地理计算; 张云倩(1992-),女,江苏扬州人,硕士研究生,主要研究方向:空间数据挖掘;骈宇哲(1989-),男,陕西铜川人,硕士研究生,主要研究方向:高性能地理计算; 周琛(1990-),男,江苏宿迁人,博士研究生,主要研究方向:高性能地理计算.
  • 基金资助:



Concerning the application requirements for the fast classification of large-scale remote sensing images, a parallel classification method based on K-means algorithm was proposed. Combined the CPU process-level and thread-level parallelism features, reasonable strategies of data granularity decomposition and task scheduling between processes and threads were implemented. This algorithm can achieve satisfactory parallel acceleration while ensuring classification accuracy. The experimental results using large-volume and multi-scale remote sensing images show that: the proposed parallel algorithm can significantly reduce the classification time, get good speedup with the maximum value of 13.83, and obtain good load-balancing. Thus it can solve the remote sensing image classification problems of the large area.

Key words: K-means algorithm, parallel computing, load balancing, data granularity decomposition, Message Passing Interface (MPI), Open Multi-Processing (OpenMP)



关键词: K-means算法, 并行计算, 负载均衡, 数据粒度划分, 消息传递接口, OpenMP

CLC Number: