Two-level parallel independent component analysis endmember extraction algorithms

doi:10.11834/jrs.20101353

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Two-level parallel independent component analysis endmember extraction algorithms
Vol. 15, Issue 6, Pages: 1202-1214(2011)
Published： 2011 ，
DOI： 10.11834/jrs.20101353

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[1]罗文斐,高连如.二级并行独立成分分析端元提取算法[J].遥感学报,2011,15(06):1202-1214.

Two-level parallel independent component analysis endmember extraction algorithms. [J]. Journal of Remote Sensing 15(6):1202-1214(2011)
[1]罗文斐,高连如.二级并行独立成分分析端元提取算法[J].遥感学报,2011,15(06):1202-1214. DOI： 10.11834/jrs.20101353.

Two-level parallel independent component analysis endmember extraction algorithms. [J]. Journal of Remote Sensing 15(6):1202-1214(2011) DOI： 10.11834/jrs.20101353.

摘要

在多对称处理器集群体系结构下进行独立成分分析并行算法研究

在共享内存模型一级并行算法基础上

通过同步、异步迭代两种方式并行计算固定点函数

分别提出具有两级并行特性的二级同步、二级异步并行端元提取算法

并结合两者的优势

进一步提出二级分组并行算法。实验评价表明

二级同步、分组并行算法在保持原算法精度的同时

大大提高了原算法的效率

体现出良好的并行计算性能

而二级异步并行算法可在节点数较少的情况下适用。

Abstract

This paper analyzes parallel ICA algorithm in symmetrical multi-processing（SMP） cluster architecture.Based on our proposed single-level share memory model parallel ICA algorithm

two-level synchronous and asynchronous parallel ICA algorithms are presented

respectively

in the manner of synchronous and asynchronous parallel iteration for computing fixed-point function.By the use of these algorithms

two-level grouping parallel ICA algorithm is also proposed.In experiment with real hyperspectral remote sensing image

synchronous and grouping parallel ICA algorithms maintenance the endmember extraction accuracy with respect to the original algorithm.Meanwhile

they also demonstrate high parallel performance and greatly improve the performance of ICA endmember extraction.Asynchronous parallel ICA algorithm is suitable for the case of small number of nodes in cluster.

关键词

高光谱遥感独立成分分析端元提取多对称处理器集群并行计算

Keywords

hyperspectral remote sensingindependent component analysisendmember extractionsymmetrical multi-processingClusterparallel computing

references

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Hyperspectral scene classification dataset based on Zhuhai-1 images

Unsupervised domain adaptive classification for hyperspectral remote sensing by adversary coupled with distillation

Dynamic selection algorithm for collaborative representation of hyperspectral remote sensing based on joint spatial information

Multifeature deep subspace clustering for hyperspectral band selection

Development of deep learning-based hyperspectral remote sensing image unmixing

相关机构

Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences

University of Chinese Academy of Sciences

College of Physics and Information Engineering, Fuzhou University

Center of Hyperspectral Imaging in Remote Sensing of Dalian Maritime University

First Institute of Oceanography, Ministry of Natural Resources

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