Principal component analysis (PCA) is applied to investigate on changes occurring in multitemporal polarimetric SAR imagery. Correlation instead of covariance matrix is used in the transformation, thus reducing gain variations introduced by the imaging system and giving equal weight to each polarization. The approach is effective when PCA is computed on images rccordcd simultaneously, as well as when it is applied to the whole set of multitemporal images.
Principal Component Analysis for Change Detection on Polarimetric Multispectral SAR Data
S Baronti;L Alparone
1994
Abstract
Principal component analysis (PCA) is applied to investigate on changes occurring in multitemporal polarimetric SAR imagery. Correlation instead of covariance matrix is used in the transformation, thus reducing gain variations introduced by the imaging system and giving equal weight to each polarization. The approach is effective when PCA is computed on images rccordcd simultaneously, as well as when it is applied to the whole set of multitemporal images.File in questo prodotto:
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