32 
Fig. 1 Location of test areas 
- further, the algorithms should transform data in 
dependently of variations in contrast. This faci 
litates the construction of mosaics and enables 
the interpretation of data across boundaries 
between different Landsat records. 
BASIC CONCEPT 
In a first step the correlation matrix was calcula 
ted to select three geologically significant bands, 
which are needed for additive color coding (Tab. 1). 
Two bands (5,6) are not useable fortnis concept. 
The relativ low correlation of SWIR-band 5 (1.6 pm) 
is caused by an overflow of the TM-detectors (on 
LANDSAT 4 and 5) and is typical for high sun ele 
vations (58° for this data of 04.17.85). Emission 
signals of band 6 are mostly depending on relief 
(sloping site) due to early morning overpass during 
heating up phase. 
Fig.2/3were calculated by use of TM band 1 (blue/ 
coded blue), TM band 4 (NIR/coded green) and TM 
band 7 (SWIR/ coded red). Although there was a good 
separability in brightness (albedo) and hues (domi 
nant color frequencies), the interpretability of 
the original composite was restricted by low satu 
ration values. This deficiency is caused by well 
balanced reflectance characteristics, related to 
the lack of strong absorption bands associated with 
most rock surfaces. 
The saturation cannot be optimized by means of 
additive (Red, Green, Blue) color processes e.g. in 
the photolaboratory and instead, the following pro 
cedure is used. Precondition is a digital transfor 
mation into three new mutually independent compo 
nents called Intensity (I), Hue (H) and Saturation 
(S) (Haydn et al., 1982) 
After a linear shift of saturation levels to the 
high saturation part within the given 8-bit range, 
the modified Saturation component and the original 
Intensities and Hues are retransformed into RGB- 
outputs. 
In order to enhance subtle variations in structu 
ral contrast, highpass-filtering was applied to the 
data using a 3 x 3 matrix and added back to the 
already I,H,S-calculated input components. The dis- 
Tab. 1 Correlation matrix for As-Sirat area 
ch 1 
ch 2 
ch 3 
ch 4 
ch 5 ch 6 
ch 7 
ch 
i 
1.0000 
0.9685 
0.9265 
0.8965 
0.7384 -0.0998 
0.7762 
ch 
2 
1.0000 
0.9804 
0.9556 
0.7772 -0.1349 
0.8437 
ch 
3 
1.0000 
0.9849 
0.8239 -0.0971 
0.8970 
ch 
4 
1.0000 
0.8650 -0.0910 
0.9094 
ch 
5 
1.0000 0.0721 
0.9042 
ch 
6 
1.0000 
0.0447 
ch 7 1.0000 
advantageous effect of reduced spectral differences 
can be compensated by the former Saturation increase 
explained above (Bodechtel & Kaufmann 1985). 
The last step is histogram computing and stretch 
for display on screen or transmission via photowrite 
system. 
Fig. 2 shows the result, which offers optimum 
conditions for lithological and structural mapping 
within one single image product. 
2.2 Classification 
Whereas the results of the image optimization pro 
cess mostly serve as a well suited image product, 
for the following interpretation procedure the 
classification methods lead to evaluation results 
which are mainly produced by the computer. Most of 
the classification procedures are working pixel- 
oriented using only spectral information. The un 
supervised methods combine the pixels with similar 
features to spectral classes, for example with the 
euclidean distance in the feature space as a cri 
terion for the discrimination. The individual 
pixels are then assigned to the class whose center 
is nearest or whose distance is smaller than a 
maximum distance. For the supervised methods accu 
rate ground truth is needed to calculate the 
features of the individual classes before classi 
fication. A classification of all pixels to one 
of the given classes will then be carried out using 
a specific classification rule. The well known 
maximum likelihood method, for example, is based 
on the calculation of the likelihood with which 
the individual pixels are members of these classes. 
A classification is also assigned to the class with 
the maximum likelihood (SWAN, DAVIS, 1978). To get 
the likelihood, statistical descriptions of the 
sample classes, such as the mean values in each 
channel and the covariance matrix, are needed. 
For the estimation of these statistics training 
fields with known landuse are introduced. The main 
problem of the supervised procedures is the se 
lection of really representative sample areas for 
the presented classes. Both kinds of classification 
methods, the unsupervised and supervised pro- 
ceuures, can also oe applied for the classi 
fication with features, which aescribe the 
texture and the shape of objects. A multispectral 
classification with textural features for example, 
car be carried out based on textural features as 
additional channels, whereas the textural parame 
ters have been calculated for each pixel in a 
window fittet around each pixel. Well known textu 
ral features are based on a statistical evaluation 
of the grey dependencies between neighouring pixels 
(HARALICK, SHANMUGAM, DINSTEIN, 1973). The calcula 
tion of so called HARALICK-parameters for each of 
the 7 Thematic Mapper bands leads to 7 additional 
textural channels. For a subsequent supervised 
maximum likelihood classification, with both the 
spectral and the textural features, a highly so 
phisticated channel selection or channel combina 
tion procedure must be carried out. Furthermore, 
a hierarchical classification procedure (WU, 1975) 
should be applied to use the full information con 
tents of all spectral and textural channels in a 
cost effective way. 
EXAMPLES 
Fig. 4 shows an unsupervised classification of the 
test area KARLSRUHE, with the euclidean distance 
as discrimination criterion using channels 3/4. 
Using a large euclidean distance (d=20 grey values) 
only a few spectral classes will be discriminated, 
whereas with smaller distances more spectral classes 
occur. For the presented Thematic Mapper data 
(color composite in fig. 3, date 7.7.1984) a small 
euclidean distance (d = 7) seems to be a favourable 
measure for the classification. Fig. 4 demonstrates 
that this euclidean distance leads to more than 10