4.3 Polygonizing and Rasterizing 
4.3.1 Vector to polygon conversion. All the digitized 
data were in segment mode. The polygonization process 
was done to make closed polygons. In ILWIS system 
we used the program called "polygonizing" to perform 
this task. For this process, a unique number was 
assigned to each polygon. 
SEGMENTS 
FINAL.DAT 
FINAL.SEG polvgonizing FINAL.POL 
FINAL .SLG ciao, FINAL.TOP 
FINAL.CRD FINAL.PLG 
4.3.2 Polygon to raster conversion. These polygonized 
parcel boundaries are still in vector mode. In order to 
process these data along with the satellite data, they 
were converted to a raster form. In ILWIS system we 
used the program called "rasterization" to perform this 
task. The program assigned an arbitrary color to each 
polygon as a label. 
FINAL.DAT 
FINAL.POL rasterization FINAL.MPD 
FINAL.TOP . 9"seseconcon FINAL.MPI 
FINAL.PLG FINAL.INF 
By selection of the raster map in the "read pixel from 
screen" menu information of all polygons can be 
retrieved simultaneouslv. Moreover we should change 
the name of the attribute information stored in ILWIS 
table file (e.g., FINAL.INF change to FINALSAV INF). 
4.4 The Final step 
4.4.1 Crossing rasterized polygons and result of 
classification. These rasterized polygons (FINAL.*) 
were crossed with the geometrically corrected 
conventional classified satellite image (e.g., ML3-237). 
Crossing can be done with the program "crossing" in the 
ILWIS system. 
4.4.2 Aggregation. The program performs different 
types of aggregation functions on the pixel values listed 
in the cross table. Aggregation functions were applied to 
the pixel values of the second map (e.g., ML3-23Z) per 
pixel value of the first map (e.g., FINAL). At this level, 
the most occurnng (predominant) pixel value within 
each polygon is assigned to that polygon. This process 
was carried out by using of the program "mapcalc" as 
follow: 
MCALC IMP := CROSS.PREDCOL([FINAL]; 
4.4.3 Crossing and error analysis. This IMP is 
generated under MCALC and crossed by the test 
sample classes ,e.g., TEST3. Confusion inatrix is shown 
in figure 6. 
540 
  
  
  
  
  
1 z 3 4 5 6 2 ü uncl {| ACC 
(Tw 4 9 0 2 5 1 16 9 TE 
210 3 0 RO 0 0 0D 0 008 
3 12 2° na 0 0 à à 0 08 
410 12 n 0 0 0 0 0 0 0m 
540 0 0 0 31 0 0 0 0 1 m 
610 0 0 08 Ug 0 ö 40 0 [om 
24d 2 0 qU B m4 0 0.30 0 00 
815 0 gn 0 0 D aea 074 
REL | 088 0.69 ? 0 0 0.5520 0.08 
average accuracy = 40.57 % 
average reliability = 34.41 % 
overall accuracy - 54.67 % 
  
figure 6 -Confusion matrix of Maximum Likelihood 
classifier affer aggregation 
As it can be seen, by developing the improved 
classification method we could reach to overall accuracy 
of 5467% in compare with 29% in conventional 
method. 
5. CONCLUSION 
Scanned aerial photos have the advantage of showing 
edges to separate different earth cover of variable 
landuse. For that matter, the classification is improved 
because one can very easily recognize all features 
including point and line, where the 100 dpi was used as 
the resolution of photo images. 
For non-flat terrain, it is essential to consider the DEM 
because the question of relief displacement should not 
be overlooked since it adversely affects the geometry of 
satellite images. If both factors are put into consideration 
in the correct way, the classification is trivially improved 
to a very good extent. The scanned aerial photos and 
DEM have facilitated the classification of satellite 
imageries though it has been difficult to be depicted in 
terms of technical documents. 
The problem in matching of vector data and raster data 
has led us to realising that DEM had propagated errors 
because topo data almost matched with SPOT raster 
data on flat terrain but the some discrepancies were 
visually noticed on mountainous terrain to disclose relief 
displacement that increases with height. 
In aggregation process in each parcel, we assigned the 
label of dominant pixels to all pixels within that parcel 
If we consider everything perfect, then we can expect 
the classification being improved. 
6. RECOMMENDATION 
Hereby, we consider the factors that increase the 
accuracy of land-cover type elevation. Accuracy and 
attention in collecting up-to-date ground truth data have 
the great importance in classification. Out-dated of 
inaccurate ground truth data, besides the changes of 
land-cover in different seasons, are the main problems. 
The best thing is to use ground truth data, aerial 
photographs, and satellite images of the same time (or at 
least at the same period of successive years). Of course, 
there are some serious problem to obtain this set of data 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996 
  
es Ca 7