A COMPARATIVE-ANALYSIS OF STANDARDIZED AND UNSTANDARDIZED PRINCIPAL COMPONENTS-ANALYSIS IN REMOTE-SENSING
A COMPARATIVE-ANALYSIS OF STANDARDIZED AND UNSTANDARDIZED PRINCIPAL COMPONENTS-ANALYSIS IN REMOTE-SENSING
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DOI：
10.1080/01431169308953962
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发表时间：
1993-05-10
影响因子：
3.4
通讯作者：
SINGH, A
中科院分区：
文献类型：
--
作者：
EKLUNDH, L;SINGH, A
本研究利用协方差和相关矩阵计算了四个数据集的主成分：月度NOAA-NDVI最大值复合数据、NOAA-LAC数据、Landsat-TM数据和SPOT多光谱数据。对结果的分析表明，与主成分分析中的协方差矩阵相比，使用相关矩阵对所有数据集的信噪比（SNR）有一致的改善。
In this study Principal Components have been calculated using covariance and correlation matrices for four data sets: Monthly NOAA-NDVI maximum-value composites, NOAA-LAC data, Landsat-TM data, and SPOT multi-spectral data. An analysis of the results shows consistent improvements in the signal to noise ratio (SNR) using the correlation matrix in comparison to the covariance matrix in the principal components analysis for all the data sets.