基于改进Cholesky分解的快速核单形体体积分析端元提取算法

Jing LI; Xiao-run LI; Li-jiao WANG; Liao-ying ZHAO

doi:10.1631/FITEE.1500244

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基于改进Cholesky分解的快速核单形体体积分析端元提取算法

常规文章 | Updated：2022-05-19

- 基于改进Cholesky分解的快速核单形体体积分析端元提取算法
- Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction
- 信息与电子工程前沿（英文） 2016年17卷第3期页码：250-257
- Affiliations：
  
  1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
  2Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China
- Author bio：
  
  E-mail: lxr@zju.edu.cn
- Funds：
  
  Project supported by the Zhejiang Provincial Natural Science Foundation of China (Nos. LY13F020044 and LZ14F030004) and the National Natural Science Foundation of China (No. 61571170)
- DOI：10.1631/FITEE.1500244
  中图分类号： TP75
- 收稿：2015-07-31，
  
  录用：2015-10-16，
  
  纸质出版：2016-03
- Accepted：
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基于改进Cholesky分解的快速核单形体体积分析端元提取算法[J]. 信息与电子工程前沿（英文）, 2016,17(3):250-257.

Jing LI, Xiao-run LI, Li-jiao WANG, et al. Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(3): 250-257.
基于改进Cholesky分解的快速核单形体体积分析端元提取算法[J]. 信息与电子工程前沿（英文）, 2016,17(3):250-257. DOI： 10.1631/FITEE.1500244.

Jing LI, Xiao-run LI, Li-jiao WANG, et al. Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(3): 250-257. DOI： 10.1631/FITEE.1500244.

摘要

目的

端元提取是高光谱图像处理中的关键步骤。研究表明，核单形体增长算法（KNSGA）是一种较好的非线性端元提取算法。然而，该算法存在两个主要问题，限制了其性能。第一，随机初始化导致算法结果不稳定；第二，算法中反复计算单形体体积导致算法时间复杂度较高。本文针对这两个问题，提出改进算法，以提高算法稳定性并降低算法时间复杂度。

创新点

本文提出采用空间像元纯度指数（SPPI）来确定KNSGA算法中的初值，提高了算法的稳定性。此外，对于KNSGA中耗时的单形体体积计算，利用改进的Cholesky分解的思想，将求单形体体积最大值转化为寻找矩阵对角元素最大值，进而降低了算法的时间复杂度。

方法

SPPI越小，则像素的纯度越高，因此将具有最小SPPI的像素作为KNSGA的初始值。原始的KNSGA提取端元的过程是循环计算单形体体积值，即每增加一个端元则计算一次端元构成的单形体体积值，直至找到所有端元为止；利用改进的Choelsky分解的快速实现算法，只需在所有端元都找到之后进行一次单形体体积计算。改进后的算法简化了算法的运算复杂度，加快了算法的实现过程。

结论

本文研究针对KNSGA的改进加速算法，利用SPPI解决初值问题，利用Cholesky分解降低计算时间复杂度。实验结果表明，提出的改进算法在算法稳定性和效率上相比原算法都有一定程度提高。

Abstract

Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm (KNSGA)

recently developed as a nonlinear alternative to the simplex growing algorithm (SGA)

has proven a promising endmember extraction technique. However

KNSGA still suffers from two issues limiting its application. First

its random initialization leads to inconsistency in final results; second

excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue

the spatial pixel purity index (SPPI) method is used in this study to extract the first endmember

eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky factorization to decompose the volume matrix into triangular matrices

in order to avoid directly computing the determinant tautologically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity

and runs faster than the original algorithm.

关键词

Keywords

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