多核CPU下的K-means遥感影像分类并行方法

针对海量遥感影像快速分类的应用需求,提出一种基于K-means算法的遥感影像并行分类方法.该方法结合CPU下进程级与线程级模式的并行特征,设计融合进程级与线程级并行的两阶段数据粒度划分方法和任务调度方法,在保证精度的基础上实现并行加速.利用大数据量的多尺度遥感影像进行实验,结果表明:所提并行方法可大大减少遥感影像的分类时间,取得了良好的加速比(13.83),并可达到负载均衡,从而解决了大区域遥感影像快速分类的问题.

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

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.