Spark平台下基于互信息计算的高光谱图像波段选择方法  

Band selection of hyperspectral image based on mutual information calculation under the Spark platform

作　　者：李俊丽[1] 马俊宏[1] LI Junli;MA Junhong(Information Technology and Engineering College,Jinzhong University,Jinzhong 030619,China)

机构地区：[1]晋中学院信息技术与工程系,山西晋中030619

出　　处：《光学技术》2024年第2期174-181,共8页Optical Technique

基　　金：国家自然科学基金(61876122);山西省基础研究计划青年科学研究项目(202103021223356);山西省高等学校科技创新项目(2021L491)。

摘　　要：随着遥感成像技术的发展和普及,高光谱图像中大量的波段使得大多数应用研究遇到休斯现象。而且随着高光谱图像数据量的快速增长,现有的传统串行算法计算复杂度较高,难以处理高维海量高光谱图像数据。针对以上问题,提出Spark平台下基于互信息计算的波段选择算法。利用熵和互信息理论定义波段相关性和多重相关性;基于Spark RDD编程模型设计数据列变换,将数据集划分为列矩阵,以降低计算负载;在Spark平台下对算法并行化,提高算法执行效率。实验结果表明,提出的算法达到了94.5%±0.5的整体分类精度,且加速性能良好,改善了数据可扩展性。With the development and popularization of remote sensing imaging technology,a large number of bands in hyperspectral images make most application researches encounter Hughes phenomenon.With the rapid growth of hyperspectral image data,the computational complexity of the existing traditional serial algorithm is high,and it is difficult to deal with high-dimensional and massive hyperspectral image data.Aiming at the above problems,a band selection algorithm based on mutual information calculation under Spark platform is proposed.The band correlation and multiple correlation are defined by entropy and mutual information theory.The data column transformation is designed based on Spark RDD programming model,and the data set is divided into column matrix to reduce the computational load.The algorithm is parallelized on the Spark platform to improve algorithm execution efficiency.Experimental results show that the proposed algorithm achieves an overall classification accuracy of 94.5%±0.5,with good acceleration performance and improved data scalability.

关 键 词：高光谱图像 波段选择 互信息计算 Spark平台 并行计算 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]