Raster Data Generalization & Simplification: Using Aggregates and Resampling and Applications (Especially for GATE-Geospatial 2022)

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Figure Shows Raster Generalization

Aggregates and Resampling

Several operations can generalize or simplify raster data. One such method is resampling, which can build different pyramid levels for a large raster data set. Aggregate is like a resampling technique in that it also creates an output raster that has a larger cell size than the input.

Some data generalization operations are based on zones, or groups of cells of the same value. For example, ArcGIS has a tool called RegionGroup, which identifies for each cell in the output raster the zone that the cell is connected. We may consider RegionGroup to be a classification method that uses both the cell values and the spatial connection of cells as the classification criteria.

Figure Shows a Lower-Resoultion Raster from the Input

An aggregate operation creates a lower-resolution raster (b) from the input (a) . The operation uses the mean statistic and a factor of 2. For example, the cell value of 4 in (b) is the mean of in (a) .

Figure Shows the Connected Region Has the Same Cell Valuea

Each cell in the output (b) has a unique number that identifies the connected region to which it belongs in the input (a) . For example, the connected region that has the same cell value of 3 in (a) has a unique number of 4 in (b) .

Applications of Generalizing or Simplifying

One of the most common applications of the Generalization tools is the process of cleaning up a classified image that was derived from remote-sensing software. The classification process often results in many isolated small zones of data that are either misclassified or irrelevant to the analysis

Generalizing or simplifying the cell values of a raster can be useful for certain applications. For example, a raster derived from a satellite image or LiDAR (light detection and ranging) data usually has a high degree of local variations. These local variations can become unnecessary noises. To remove them, we can use aggregate or a resampling technique.

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