TY - JOUR
T1 - Some invariance properties of the minimum noise fraction transform
AU - Berman, Mark
AU - Phatak, Aloke
AU - Traylen, Anthony
PY - 2012
Y1 - 2012
N2 - The Minimum Noise Fraction (MNF) transform is widely used in the remote sensing and image processing communities, because it is usually better than the Principal Components (PC) transform at compressing and ordering multispectral and hyperspectral images in terms of image “quality”. The MNF transform is also invariant to invertible (i.e. non-singular) linear transformations of multispectral/hyperspectral data, a property not shared by the PC transform. This general invariance property of the MNF transform is proved. Three examples of the general invariance property are provided and discussed: (i) invariance to scaling, (ii) invariance to certain types of background correction, and (iii) invariance to different types of noise.
AB - The Minimum Noise Fraction (MNF) transform is widely used in the remote sensing and image processing communities, because it is usually better than the Principal Components (PC) transform at compressing and ordering multispectral and hyperspectral images in terms of image “quality”. The MNF transform is also invariant to invertible (i.e. non-singular) linear transformations of multispectral/hyperspectral data, a property not shared by the PC transform. This general invariance property of the MNF transform is proved. Three examples of the general invariance property are provided and discussed: (i) invariance to scaling, (ii) invariance to certain types of background correction, and (iii) invariance to different types of noise.
UR - http://handle.uws.edu.au:8081/1959.7/550494
U2 - 10.1016/j.chemolab.2012.02.005
DO - 10.1016/j.chemolab.2012.02.005
M3 - Article
SN - 0169-7439
VL - 117
SP - 189
EP - 199
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
ER -