Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

20 2. Background and Related Work
Current database technology for GIS and remote-sensing imaging applications employ
multi-scale image pyramids to improve performance of scaling operations on 2D
raster images [51, 70, 82]. Image pyramids is a technique which consists of resampling
the original dataset and creating a number of copies from it, where each copy is
resampled at a coarser resolution (Fig. 2.4). The pyramid consists of a finite number
of levels that differ in scale by a fixed step factor, and are much smaller in size than the
original dataset but adequate for visualization at a lower scale (zoom ratio). Common
practice is to construct pyramids in scale levels of a power of 2, yielding scale factors
2, 4, 6, 8, 16, 32, 64, 128, 256, and 512. When more detailed data are needed, or
when it becomes necessary to access the original image, a better access speed can be
achieved by accessing the smaller piece of the original data, if the original data are cut
into smaller pieces. A restricted area of the image, instead of the entire image, is then
accessed.
Figure 2.4. Image Pyramids
Pyramid Construction
The construction of pyramid layers requires resampling of original image cell values.
Resampling interpolates cell values or otherwise assigns values to cells of a new
raster object. It results in a raster with larger or smaller cells and different dimensions.
Resampling changes the scale of an input raster, and is used in conjunction
with geometric transformation models that change the internal geometry of a raster.
The following are the most popular interpolation methods [34]:
• Nearest neighbor is the resampling technique of choice for discrete (categorical)
data since it does not alter the value of the input cells [64]. After the cell’s center
on the output raster dataset is located on the input raster, the nearest neighbor
assignment determines the location of the closest cell center on the input raster
and assigns the value of that cell to the cell on the output raster.
• Linear interpolation is used to interpolate along value curves. It assumes that
cell values vary in proportion to distance along a value segment: v = a + bx.
Linear interpolation may be used to interpolate feature attribute values along a
line segment connecting any two point value pairs.