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sparsely populated Central Siberia where remote sensing becomes the only reliable
source of information for decision-making, taking into account underdeveloped
transport infrastructure in this region.
It is known that the number of attributes of a separate ISD file can reach several
hundred. ISD files from different satellites generally contain different groups of
attributes. A structure of information queries of the user of the system differs consi-
derably as well. It is due to the differences in purposes of using the system (scientific,
educational, industrial), solved problems, operational experience and other factors,
characteristic for this or that user of the system.
Thus it is topical to discuss questions concerning adaptation of satellite images da-
tabase to the constantly changing requirements of users and appearance of new data
representations caused by new-type sensors registered in the system.
In this work we offer a technology of automated RDESI adaptation to the con-
stantly changing external conditions.
2 A Review of the ISIT SFU (Institute of Space and Information
Technology, Siberian Federal University) Satellite Images
Database
At present we have data collected from optical satellites: Landsat 4-5 sensor TM,
Landsat 1-5 sensor MSS, Landsat-7 sensor ETM+, SPOT-2/4 sensor HRV and
HRV(IR), Terra and Aqua, sensor MODIS. The support of following metadata for-
mats has been realised:
satellite SPOT-4; extension .dim; metadata standard DIMAP is based on XML
satellites Landsat 4-5 TM ;extension .txt; syntax PVL;
satellite Landsat 7 ETM+ ; extension .met; syntax PVL;
satellite QuickBird; extension .IMD; syntax PVL;
satellites Terra\Modis, Aqua \Modis; extension .txt; syntax PVL;
Each satellite image in a database correlates with a certain territory and is pre-
sented as a set of raster layers of various spectral channels, generally with different
spatial resolution, united into GeoTIFF format, or one of its analogues and ISD ac-
companying it, describing different attributes of the given image (scene). Indexation
of already stored and newly arriving satellite images is realized by means of separate
modules which parse metafiles of its type of satellite data and select necessary image
attributes and characteristics already specified. The data from different sensors has
different sets of metadata attributes. For example, attribute "cloud cover" is absent in
SPOT-4 satellite products, but is present in some products of Landsat and Ter-
ra\Modis.
With the course of time the number of the satellites launched increases, the capac-
ity of sensors installed on board widens. In this connection attribute sets for one scene
are extended. Besides, when new users are added to the system, attribute sets for
searching necessary satellite images are modified.
Traditional ways of building systems of processing of satellite information call for
writing (modification) programs for parsing of metadata for each of the fixed cases of
the system extension. The alternative way is to embed parsers from other producers
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