|Table of Contents|

Spectral Detecting of Water Components in the TurbidWater Body by Principle Component Analysis(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年02期
Page:
68-
Research Field:
计算机与信息工程
Publishing date:

Info

Title:
Spectral Detecting of Water Components in the TurbidWater Body by Principle Component Analysis
Author(s):
Cong Maoqin; Luo Hong; Wei Yuchun
School of Geography,Nanjing Normal University,Nanjing 210023,China
Keywords:
principle component analysis; turbid water; component identification; singular value decomposition; nonlinear iterative partial least squares
PACS:
X131.2
DOI:
10.3969/j.issn.1672-1292.2019.02.009
Abstract:
The turbid water is an important research object in water color remote sensing,whose spectral characteristics are determined by the phytoplankton(chlorophyll-a concentration),inorganic suspended matter and dissolved organic matter. Using spectrum data collected from field measurement in Taihu Lake,the paper compares the performance of identifying the components from turbid water spectrum among three methods,eigenvalue decomposition(EVD),singular value decomposition(SVD),nonlinear iterative partial least squares(NIPALS),and whitening post-processing,taking the covariance matrix and the correlation matrix as input. The results shows that SVD with input of correlation matrix presents the better performance,that the first three principal component loadings of SVD have 98.8% cumulative contribution rate,and represents component information about the water,chlorophyll-a dominant and suspends sediment dominant,respectively,while the whitening does not show an obvious effect.

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Last Update: 2019-06-30