etc. principal component (PC1, PC2,...), denoted by y XNosya» (Ye The 
: Y : : E n 
new images contain the same information as the original OneB, however 
    
concentrated in the first component (PC1), to a lesser extent in the se- 
cond component (PC2) and so forth. Hardly any information is contained 
in the higher order components. "Information" thereby is variation of 
gray tone corresponding to transformed radiance levels, or in another 
context the significance of the pattern apprearing on the image for in- 
terpretation. 
The transformation from x. to y. is rather simple, consisting for each 
pixel of a weighted average Of each spectral dimension i: 
„= Dn.. X, Fü. XA, T... t 4. X 
Ji 31 1 12 2 in n 
The weights (coefficients a..) form a rotation matrix which diagonalizes 
a covariance matrix estimated over a subset of the image either selected 
automatically or specified by the interpreter. Many discussions of the 
method exist, e.g. Landgrebe et al., (1972), Ready and Wintz (1973), 
Mulder and Hempenius (1974) and Anuta (1977). 
The advantage of PCT applied to MSS data is twofold: 
(a) An effective compression of the data is achieved. For the example 
of the 4 channels of Landsat, almost all information is contained 
in the first two principal components. With aircraft MSS, a drastic 
reduction from e.g. 12 channels to the first few PC's is possible. 
(b) This reduced dimensionality allows the operator to define, on the 
basis of a proper sample set, the best products for interpretation 
(compare section 4.3). 
With Landsat, PCT has shown valuable results not only by dimensiona- 
lity compression, but also as a method of enhancing certain phenomena. 
In the view of some image processing experts, however, results of PCT 
are somewhat unpredictable.These experts may call it a hit or miss tech- 
nique.The so-called deficiencies of PCT relative to information extraction 
can be alleviated by the addition of training: The image is appropriately 
sampled using interpretation expertise and the PCT is based on the manually 
selected sample. One may rightfully generalize that image processing tech- 
niques should always be applied under the control of the interpreter and 
that without such control (training) most methods are hit or miss techniques. 
Transformations that maximize the variation between identified class means 
in certain of the transformed components have been termed canonical 
and have been applied successfully to the geologic analysis of multi- 
spectral images (Podwysocki et al., 1977). Linear discriminant analy- 
sis procedures have also been used effectively to deal with a large 
multispectral data base (Siegal and Abrams, 1976; Jennrich, 1977). 
4.2.2 Ratioing 
Enhancement of multiple, in particular multispectral images using 
ratio Bj/Bj of spectral bands i, j has been extensively applied in 
planetological interpretation of lunar images (Bi, Bj are gray values of 
bends i, j). These methods can be analog using photographic techniques 
(Mulder and Donker, 1977). With the advent of digital data ratioing is 
applied digitally to remove effects of the spectral brightness, leaving 
entirely the 
spectral differences. Thus hill shadow (variations of