An example of principal component analysis applied to correlated images Maciejewski, Anthony A. ; Roberts, Rodney G. "This work was supported by the National Imagery and Mapping Agency under contract no. NMA201-00-1-1003." The use of Principal Component Analysis (PCA), also known as Singular Value Decomposition (SVD), is a powerful tool that is frequently applied to the classification of hyperspectral images in remote sensing. Unfortunately, the utility of the resulting PCA may depend on the resolution of the original image, i.e., too coarse-grained of an image may result in inaccurate major principal components. This work presents an example of how the major principal component obtained from the PCA of a low-resolution image may be refined to obtain a more accurate estimate of the major principal component. The more accurate estimate is obtained by recursively performing a PCA on only those pixels that contribute strongly to the major principal component. Colorado State University. Libraries 2001 text ; image application/pdf ECEaam00089.pdf FACFECEN100089ARTI eng c2001 IEEE
An example of principal component analysis applied to correlated images
Maciejewski, Anthony A. ; Roberts, Rodney G.
"This work was supported by the National Imagery and Mapping Agency under contract no. NMA201-00-1-1003."
The use of Principal Component Analysis (PCA), also known as Singular Value Decomposition (SVD), is a powerful tool that is frequently applied to the classification of hyperspectral images in remote sensing. Unfortunately, the utility of the resulting PCA may depend on the resolution of the original image, i.e., too coarse-grained of an image may result in inaccurate major principal components. This work presents an example of how the major principal component obtained from the PCA of a low-resolution image may be refined to obtain a more accurate estimate of the major principal component. The more accurate estimate is obtained by recursively performing a PCA on only those pixels that contribute strongly to the major principal component.
Colorado State University. Libraries
2001
text ; image
application/pdf
ECEaam00089.pdf
FACFECEN100089ARTI
eng
c2001 IEEE