SVD vs. PCA: Comparison of performance in an imaging spectrometer

Abstract

The calculation of basis spectra from a spectral library is an important prerequisite of any compact imaging spectrometer. In this paper, we compare the basis spectra computed by singular value decomposition (SVD) and principal component analysis (PCA) in terms of estimation performance with respect to resolution, presence of noise, intensity variation, and quantization error. Results show that SVD is robust in intensity variation while PCA is not. However, PCA performs better with signals of low signal to noise ratio (SNR). No significant difference is seen between SVD and PCA in terms of resolution and quantization error.