Big Data by Karimi Hassan A

Author:Karimi, Hassan A. [Karimi, Hassan A.]
Language: eng
Format: epub
Published: 0101-01-01T00:00:00+00:00

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Big Data: Techniques and Technologies in Geoinformatics

7.3.1.1 Windowing in Raster Data

Raster data in a geospatial context are a tessellation of the plane P often 2D into a number of connected cells. A typical example of raster data is the pixels of an image such as satellite data. When considering some location P of the raster, windowing i, j

takes into account neighboring cells shown in Figure 7.1. The function determining

which neighboring cells to consider and how they should be weighted and trans-

formed is called a window function. In the simplest case, a four-adjacency window

would consider the four pixels: left, right, above, and below a pixel as shown in

Figure 7.1. More complex windowing functions can be used to consider further pix-

els or even transformations of combinations of pixels and other properties such as

how edges of the raster or missing values should be dealt with.

Windowing of raster data has two main effects: First, it allows the autocorrelation

of neighboring spatial and temporal data in, for instance, a raster cube (time series (a)

(b)

(c)

FIGURE 7.1 (a) A typical windowing function centered on a pixel in the middle of the white box. The window could apply any function to the surrounding pixels. (b) A larger windowing function centered on the same pixel. (c) A very simple windowing function centered on the same pixel and considering only the left, right, above, and below pixels.

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