3.5 Multispectral classifications
A hybrid method has been used for multispectral classification.
First, an unsupervised classification of the color composite
SPOT HRV and IKONOS images has been realized (with
ISODATA algorithm), where the selected 10 classes are
defined. Several confusions occurred in some areas of the
image: for example built areas and uncovered soil areas. In
order to improve classes definition a vegetation map at 1:2,000
scale, realized in 1993 has been used. After classes regrouping
followed a hybrid supervised classification for 6 classes: streets
and paths, parceled areas with buildings, lake surface, trees
vegetation, grass vegetation and uncovered soil. The accuracy
classification was estimated using the standard, single-data,
qualitative accuracy assessment procedures for each image.
Producer and user accuracy were calculated for each change
class, along with the overall accuracy (error matrix and Kappa
Index of Agreement analysis) (Congalton and Green, 1996).
Global accuracy for the obtained classifications ranged between
90.44% and 96.53%. Kappa coefficients ranged between 0.86
and 0.93.
4 CONCLUSIONS
Digital processing techniques in the Bucharest study area,
during 1964-2007, highlight the following:
e The available medium and high resolution satellite images
allow a cinematic multisensor approach. The high
resolution Corona image scanned with care, provides high
levels of detail on ground features. In this case three-
dimensional information can be extracted from the
CORONA imagery using only a small number of GCPs.
Corona and IKONOS images are important means for
changes detection in urban and peri-urban areas. Also
SPOT-HRV medium scale images ensure a satisfactory
level of accuracy for monitoring changes detection in urban
areas;
e In order to complete information for these images,
historical aerial photographs at 1:5,000 scale, acquired in
1994 (from the same date) have be used for correction of
satellite images. Applied techniques (preliminary
radiometric and geometric processing, data compression,
contrast and edge enhancement, multispectral
classification, post-classification processing) assured also
the maximum accuracy in data processing (without altering
the initial information) and in the results of interpretation;
» Changes between the imagery were determined partially
through visual interpretation, by elements such as location,
size, shape, shadow, tone, texture and pattern (Corona
image), partially by six classes hybrid supervised
classification (SPOT HRV and IKONOS): streets and
paths, parceled areas with buildings, lake surface, trees
vegetation, grass vegetation and uncovered soil. Subsets of
the images were generated within and near the city where
changes were evident. Noticeable changes in land cover
between the imagery were manually or automatically
digitized and imported into a GIS, where changes could be
visualized and analyzed. Other changes are presented in the
form of thematic maps highlighting changes of urban
development and ecological comfort parameters. The first
25 years of the study period (1964-1989) are characterized
by growth of industrial areas, high density apartment
buildings residential areas and leisure green areas by
demolition of houses or cultural heritage areas (22 hundred
years old churches and other architectural monuments) -
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
see SPOT HRV and IKONOS images. The second period
of 18 years (1989-2007) highlighted considerable growth
of residential areas in the city neighborhood,
simultaneously with diminish of green areas and massive
deforestation in confiscated areas before 1989 by
communist regime and returned to the original owners.
The study demonstrates once again that remote sensing and
photogrammetry deliver means of gathering useful information
regarding present status and future urban trends. Continuous
analysis of repetitively acquired data, in the same area allows
urban and extra urban areas change detections, easing the
process of finding proper solutions and revision of local politics
in urban development in accordance with UE regulations.
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[1]CoronaSummary
http://www fas.org/spp/military/program/imint/corona. htm
(25/01/2012)
[2]KH-4CameraSystem http://www. fas.org/irp/imint/docs/kh-
4 camera syatem.htm (25/01/2012)
