PostGIS Raster : Extending PostgreSQL for The Support of ... - CoDE

Figure 4.42: Web application with out-database raster [23].
• Simplified backup: since raster images in the filesystem that is not expected to be edited
and only its metadata is registered in the database, in this way, out-database storage can avoid
useless database backup of large data.
In contrast, the traditional in-database raster is related to editing and analysis applications because
of the following advantages:
• Fast analysis: analysis operations performed on raster data (such as dum a raster as polygons
using ST_AsPolygon(), find intersection between raster and vector/raster with ST_Intersections()
and etc.) require working on pixel values. The structure of raster data is nothing else a grid
of two dimensions of cells, where value of each cell represents value of the corresponding pixel.
Obviously, execution time of these operations is faster on in-database rasters since there is no
need to extract the pixel values from an image file of JPEG or TIFF format.
• Single storage: no matter what if data belongs to raster or vector type, PostGIS Raster allows
a single dataset storage. There is no need to store the raster data separately from the vector
data, they can be placed alternatively in the same dataset.
• Multi-Users edition: multiple users can modify raster data at the same time as PostGIS
database handles concurrent editing on in-database rasters.
4.15.2 Registering Out-database Raster
Registering out-database raster is done via the -R option when loading raster image file into
database.
Example:
python raster2pgsql.py -r c:/imagesets/landsat/image.tif -t landsat -R
Where the key option -r specifies the input raster image, the -t option specifies the name of
rater table that contains the corresponding raster and the -R option indicates that the raster image
is registered as a filesystem.
4.15.3 Storing In-database Raster
Like geometry type, raster type is a complex PostgreSQL type composed of many attributes. A
raster attribute may be composed of many bands having a common size, pixel size and georeference.
Besides this, additional information is needed to interpret the image data, such as a pixel type and
a nodata value. The storage used for raster bands is band sequential (BSQ), which is one of three
primary methods for encoding image data for multiband raster images. BSQ is not itself an image
format but is a method for encoding pixel values of an image, where each line of the data is followed
immediately by the next line in the same band. The BSQ data organization is optimal for spatial
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