ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001 
33 
id GIS data, with the 
lery in an integrated 
i by utilising a digital 
J in the procedure is 
rried out for both the 
-inally, the nature of 
learly identified. 
. INFORMATION 
ELUTE IMAGERY 
YSTEM. 
mapped up to now 
national scales has 
oatial resolution of 
er maps generated 
jery, with a spatial 
if detail in the scene 
ailability of recent 
tial resolution has 
leometric detail and 
397) for local and 
ly to generate other 
is enhanced (11 bit 
*re generally limited 
ion sensors (seven 
jssociated with an 
ally, an increase in 
mage) generating a 
ation on a per-pixel 
les classify land use 
als within an image, 
lost similar spectral 
suits that are 'noisy' 
covers. 
sification (so called 
are classified as 
spectral and spatial 
pe of the fields and 
ormation about the 
pies, roads fields. If 
nd registered to the 
vithin boundaries of 
elds. For instance, 
r IKONOS bands of 
as features defining 
Jormally, the use of 
thin a geographical 
ides facilities for 
3mple, checking the 
, ensuring that lines 
cal lines that end 
Fig. 1 "Color composite image of IKONOS data - Fasano (Italy) 
All these considerations suggest that replacing the coarser 
spatial resolution satellite sensor imagery with finer ones would 
lead to an increase in per-field classification accuracy, especially 
for relatively small sized fields (Harris and Ventura, 1995; 
Westmoreland and Stow, 1992). 
However, the method used to integrate remotely sensed imagery 
with field boundaries is also meaningful. Such integration may 
be carried out in three stages (White, 1995): 
before classification (pre-classifierstratification), 
during classification (classifier modification)] 
after classification (post-classifiersorting/). 
Many examples of these procedures employed only the latter 
two. Westmoreland and Stow (1992) integrated, in a single 
stage, cartographic data with remote sensed imagery during 
classification to assess land use change on per-field basis. 
Alternatively, other studies obtained land cover on a per-pixel 
basis before integrating the classified image with cartographic 
data for per-field classification (White, 1995). 
The ways with which the two data models, raster and vector, are 
integrated in a RS/GIS system can be summarised in: 
a) separate database, cartographic and image processing 
systems with facilities to transfer data between them; 
b) two software packages (image processing and GIS) with a 
shared user interface and dynamic links. 
c) a single software package with shared processing. 
In this study the b) type of implementation is performed, because 
of the high level of the customisable statistical and logical tools 
and the possibility of modifying per-pixel classification using the 
spatial information derived from external data. 
3. THE STUDY AREA AND THE DATA SOURCES. 
This research was carried out in the Italian territory (about 4 Km 
per 5 Km) which includes part of the municipal district of Fasano 
(Brindisi) in the Apulia region (Fig. 1). It represents a critical area 
because of urbanisation over the last thirty years, still continuing 
now. Such evolution is been apparent with long period built-up 
area phenomena on a local scale (second home), with rise and 
spread of tourist places (“Safari” zoo, fair grounds) and, finally, 
with agricultural transformations (olive-groves dominating over 
natural areas, such as ilex and Mediterranean bush). 
The géomorphologie and settling features, with the town and the 
ancient fortified farms that are located on the plain (100 m a.s.l. 
quota) and the villas that are strewn around the hills (300-400 m 
a.s.l. quota), justify the choice of this area because it permits the 
testing of the validity of well-established methodologies of 
classification on a over-regional scale, but barely investigated on 
new generation satellite sensors imagery. Moreover, such 
studies allow opening of new unexplored techniques applicable 
on a large scale by adding meaningful inputs to those 
multidisciplinary studies connected to decision-making and 
planning activities. 
The data sources acquired for the analysis consisted in: 
the 4 bands IKONOS raster data (Acquisition Date/Time: 
2000-05-12 / 09:14), processed using the remote sensing 
image processing system ERMAPPER; 
the digital topographic map in a scale of 1: 5000 (Date 
1998), stored and processed by means of the geographical 
information system ARC/INFO; 
the ground reference data obtained with a land use survey 
(January 2001). 
4. METHODOLOGY. 
4.1 Data processing 
To facilitate the integration of the IKONOS and vector data it was 
necessary to register both data sets to a single map coordinate 
system (Mather, 1995), in this case Gauss-Boaga map 
projections and Roma40 datum, by identifying 30 common