SATKI. Exercise 2. Supervised classification of multi- 
spectral satellite images. 
This exercise deals mainly with supervised classification. Two 
data-sets, a 4-band Landsat MSS image and a 3-band SPOT XS 
image are used. Again CURBOX and SUBSET are used to 
generate secondary subsets. Training areas are picked by 
DIGSCRN. As in the previous exercise, also these images cover 
the area where the University is located, so the students have 
a fairly good idea of what is present on the ground. Signatures 
are generated with SIGEXT and SIGMAN, and ELLIPSE is 
used for an evaluation of the signatures. In MAXCLAS, the 
students themselves decide which classification algorithm to 
use. Supplementary texts are written by ANNOTAT as shown 
in Figure 2. This "Result Screen-Image" contains the original 
MSS subset as a False Colour Composite (FCC), the result of 
the MSS classification and also the result of the classification 
based on the SPOT XS data. The image in the lower right hand 
corner of Figure 2 is a small portion of the classified MSS 
image, enlarged to the same scale as the XS classification 
result. Since the MSS image is geocoded and the XS image has 
only been through a system-correction (level 1B), the two 
images are slightly differently orientated. It should also be 
mentioned that the functions COLORMOD and RECODE are 
introduced in this exercise. 
  
Ea 
  
Figure 2. Supervised classification of Landsat MSS and SPOT 
XS subscenes. 
SATKI. Exercise 3. Satellite mapping / Raster-GIS. 
The purpose of this exercise is to demonstrate the close 
connection which exists between satellite images in digital 
format and other types og geo-data in raster format. The 
example in this exercise is taken from a snow mapping project 
carried out in collaboration between Fjellanger Widerge A.S. (a 
privately owned surveying and mapping company), Norwegian 
State Power Board, Norwegian Computing Centre and 
Norwegian Hydrotechnical Laboratory. A -.GIS-file showing 6 
classes of snowcover is derived from the NIR band in a NOAA 
AVHRR image. The image, as well as the -.GIS-file, are 
referenced to the UTM coordinate system. The resolution in 
terms of pixel size is 1 x 1 km. The State Mapping Authority 
has provided a digital terrain model in raster format with 
corresponding resolution covering the same area. Originally this 
384 
DTM is a -.LAN-file. By the aid of CURSES, the studens pick 
X, Y and Z coordinates and compare the results with a 
topographic map. The terrain-elevation file is also used to 
demonstrate various possibilities in the COLORMOD function. 
The terrain-elevation file exists also as a -.GIS file with 6 
classes. In the last part of the exercise, MATRIX is used to find 
those areas which fulfil certain snowcover- as well as terrain 
elevation requirements. A third -.GIS-file which defines the 
"Nore" catchment area, makes it possible to show only the part 
of the actual area which is inside the catchment. Figure 3 
shows the "Result Screen-Image". 
  
raster 
Figure 3. Digital terrain elevation data and 
area in 
representation of "Nore" catchment 
combination with NOAA AVHRR data. 
SATK2. Exercise 1. Satellite image geometry. 
In the first part of the exercise, CURSES in combination with 
a small scale atlas-map, is used to compare the scale in the 
central and in the marginal part of a NOAA AVHRR image 
which has not been geometrically corrected. In this way the 
effects of a large viewing angle and the curved earth surface 
are illustrated. Figure 4a shows the "Result Screen-Image" from 
this part of the exercise. 
Figure 4a. Geometric properties of satellite imagery illu- 
strated with subsets of a NOAA AVHRR scene.