Nas
airs
f a
del
sult
| of
fied
ates
phic
3PS
ere
ble,
the
rol
one
cted
ned
odel
ted,
ined
odel
co-
not
"on
LED
was.
n of
A is
phic
itour
cale
,000
(dpi)
ation
cale
ters
34)
* Computation of a 20 meters grid HEIGHT DATA
file by interpolating contour lines and spot heights.
The above derived DTM was mosaiced with the existed
SPOT STEREOPAIRS DTM in order to complete the
HEIGHT OATABASE.
The absolute accuracy of the SPOT digital elevation
model depends also on the scale of the maps used for
the definition of the ground control points. SPOT digital
elevation model was registered from 1:50,000 scale
topographic maps and respectively has 30 meters plane
accuracy and 10 meters height accuracy. About 2000
points were checked, distributed in the whole country and
the errors in height are summarised at the following table:
= EZ M rt rte que pa sta
xe ET E Sor MELLE MEC 2 EC dv OT
e Rh. P URP rentrent I Veloso EN
Table 2 : Errors in height from 2000 checked points
Finally, using the above produced DTM the SPOT-P,
SPOT-XS and LANDSAT TM were orthorectified.
3.2 Clutter Database
The CLUTTER DATABASE was extracted from
multispectral satellite orthoimages, SPOT XS and
LANDSAT TM (Fig.2). by classification and photo-
interpretation techniques.
CC] SPOT images
M LANDSAT images
Figure 2 : Clutter Data
The Feature Space (ERDAS Imagine 8.2) decision rule
was used for the classification of the multispectral
images. Feature space image were used to define the
training sample. The advantages of this method over the
917
EE Advantage sas
traditional ones are . that feature space is a non-
parametric signature, the decisions made in the
classification process have no dependency on the
statistics on the pixel and helps. to improve classification
accuracy's for the non-normal classes.
e »gixel$ in C215 ©
a = 3ixelsin cas: 2
* = Qixals iv Cass 5
Band 8
shades hie value
8and A
daca Me values
Figure 3
The classified classes were :
e forests
e waters
e Open areas
E urban
e suburban
Urban and suburban classes were extracted from the
photo-interpretation of panchromatic and multispectrai
images.
The advantages and disadvantages of using feature
space signatures can be briefly presented at the table
below:
c
Sage)
Helpful for the first pass, | Feature Space image may
broad classification. be difficult to interpret.
An accurate way to classify
classes with non-normal
distribution.
Features may be more
visually identifiable, which
can help discriminate
between classes spectrally
similar and it is hard to
differentiate with
parametric information.
Feature space method is
very fast.
Table 3 : Advantages of using Feature Space Signatures
3.2 Vector Map Data
Vector Map Data were extracted from the panchromatic
and multispectral orthoimages by interactive on screen
digitisation techniques.
The following planimetric features were obtained:
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996