一种高精度并行主偏度分析算法及其在遥感图像中的应用  

A High Precision Parallel Principal Skewness Analysis Algorithm and Its Application to Remote Sensing Images

作　　者：王大虎 刘畅 王健 姚锴 张振[3] WANG Dahu;LIU Chang;WANG Jian;YAO Kai;ZHANG Zhen(Institute of Space Information Innovation,Chinese Academy of Sciences,Beijing 100190,China;School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100094,China;Chinese People's Liberation Army Aviation Academy,Beijing 101123,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100190 [2]中国科学院大学电子电气与通信工程学院,北京100094 [3]陆军航空兵学院,北京101123

出　　处：《电子与信息学报》2023年第10期3492-3501,共10页Journal of Electronics & Information Technology

摘　　要：主偏度分析(PSA)作为主成分分析(PCA)的一种3阶推广,常用于盲图像分离、SAR图像去噪以及高光谱特征提取等。但现有PSA算法只能得到近似解,这会影响图像后续处理的精度。针对这一问题,该文在现有PSA算法基础上,提出了一种高精度并行主偏度分析(PPSA)算法。PPSA算法充分考虑数据结构,选用协偏度张量的全部切片的特征向量作为迭代的初始值,可以准确地得到实际解。仿真实验以及实际遥感图像实验验证了PPSA算法的有效性与优越性。Principal Skewness Analysis(PSA),as a third-order extension of Principal Component Analysis(PCA),is often used for blind image separation,SAR image denoising,and hyperspectral feature extraction.However,the existing PSA algorithm can only obtain approximate solutions,which will affect the accuracy of subsequent image processing.In view of this problem,a high-precision Parallel Principal Skewness Analysis(PPSA)algorithm based on the existing PSA algorithm is proposed.The PPSA algorithm considers fully the data structure,and selects the eigenvectors of all slices of the co-skewness tensor as the initial value of the iteration,which can accurately obtain the actual solution.Simulation experiments and actual remote sensing image experiments verify the effectiveness and superiority of the PSA algorithm.

关 键 词：图像分离 图像去噪 主偏度分析 高精度 并行 特征提取 

分 类 号：TN911.7[电子电信—通信与信息系统]