35
ause, by using the
> (represented by
ing data to identify
Df global accuracy,
was performed, in
with the correction
il context (digital
ial context is the
>nly one land cover
id from the digital
ie GIS environment
ition in the image
assification filtering
to allow speckle
r
from the randomly
by-pixel basis with
ellite imagery. This
5 the results of both
latrices (Tab. 1), k
inclusion of the
! IKONOS image
f the results (from
ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
Class
Description
1 Ml SOWN GROUND
2 ARTERIAL ROAD
3 HI MEDITERRANEAN BUSH
4 Hi HIGH DENSITY BUILDINGS
5 UNCULTIVATED GROUND
6 OLIVE-GROVE
7 fgm COUNTRY ROAD
8 T: ; ^3 ASPHALT ROAD
9 |i| LOW DENSITY BUILDINGS
Fig. 3 “Per-field classified study area"
The per-pixel method had produced misclassifications, not only
as a consequence of internal variability within fields, but also due
to the utilisation of land use as opposed to land cover classes.
Per-field classification was able to overcome these problems in
some instances, but in others, such as inexact geometric
registration and errors in the original vector data, they meant in
the selection of uncorrected classes.
5. CONCLUSIONS
Flexibility of the integration process in the present software
packages and the high spatial resolution of the new generation
satellite imagery may globally lead to an increase in geometric
detail and accuracy with which land cover can be mapped over
images of coarser spatial resolution in which, as many recent
researches attest, the presence of mixed pixel is the dominant
problem to resolve.
At present, an increasing amount of geographical data are
stored in geographical information systems. This data could
prove useful in the processing of remote sensing images. In
addition, remote sensing images may be considerably applied to
store and update data in a GIS.
The per-field classification developed in this paper should be
considered as a test to validate on a local scale well established
methodologies of classification applied at on over-regional scale
and future researches are advisable to reduce sources of
resulted misclassifications. Moreover, such types of studies
allow opening of new unexplored techniques applicable on large
scales by adding meaningful inputs to those multidisciplinary
studies connected to decision-making and planning activities.
6. REFERENCES
Aplin, P., Atkinson, P. M. and Curran, P. J. (1997). Fine spatial
resolution satellite sensors for the next decade. International
Journal of Remote Sensing, 18, pp. 3873-3881.
Carbone, G. J., Narumalani, S. and King, M. (1996). Application
of remote sensing and GIS technologies with physiological crop
models. Photogrammetic Engineering and Remote Sensing, 62,
pp. 171-179.
Cowen, D. J., Jensen, J. R., Brensnahan, P. J., Ehler, G. B.,
Graves, , D., Huang, X., Wiesner, C. and Mackey, Jr, H. E.
(1995). The design and the implementation of an integrated
geographical information system for environmental applications.
