| = io "ET ifi 
| Principal components 1 (photo 4) resolves differences in the agricultural field gu 
pattern and land use classes, emphasizes river courses, built up areas and the and 
outline of lakes against forests, as well as differentiating various types Piel 
within forests. These are all factors which are important for interpretation diti 
| in a cultured landscape. Principal components 2 (photo 2) lacks the discerning i3; 
| i capability of forests and lakes, but makes field parcelling even clearer. Espe- 1) I 
cially linear structural elements like highways indicate good structural resol- plai 
ving power. Both principal components can be superimposed as a FCC with a max- qual 
imum of information density, but for reproduction reasons it is not shown here. Soil 
Contrast enhancement of edges by high- rick 
pass filtering was an additional me- 2) M 
thod for emphasizing patterns and plai 
structures. Applied onto the princi- se 
| pal component 2 (photo 5), it shows Hann 
| striking results on field parcelling ate 
patterns (photo 6). This is achieved 
by brightening or darkening sharp tran- 6. I 
sitions between bright and dark in their 
respective direction. The brightening To i 
or darkening factor applied is inversely tior 
proportional to the frequency of bright clas 
| to dark changes. comt 
| scal 
| | Ratioing here is used to discriminate Spec 
| spectral bands bearing nearly similar Bier 
hi information, i.e. bands 4 and 5 as well TET 
| as band 6 and T. The algorithm used ; ie So 
| is the difference between the spectral KG n TRE ; alo 
: values of two bands divided by their ‘Photo 6: Contrast enhancement of edges diy 
sum, and this quotient multiplied by on the basis of photo 5 (black + white) sifi 
| a constant. The ratio between band 4  - ch 
and 5 (photo T) shows to advantage ted 
differences in crops, bare soils and forests, 
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Photo T: Ratio between channels 4 and Photo 8: Ratio between channels 6 and sr 
5, 28. 8. 75, (black + white) T, 28. 8. 75, (black + white) Tore 
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