Altering the resolution of a raster

Available with Spatial Analyst license.

There are several tools available that alter the resolution of an existing raster. If you have one raster at a finer resolution than other rasters, you may want to resample the finer resolution raster to the same resolution of the coarser ones, making all the raster datasets the same resolution. This speeds up processing and reduces data size. Unlike the Cell size setting in the analysis environment, the resolution altering tools are applied only to the resultant raster. The top graphic shows a raster of an area at a fine resolution, while the graphic immediately below it shows a raster of the same area with a coarser resolution.

Raster of an area at a fine resolution
Raster of an area at a fine resolution
Raster of an area at a coarse resolution
Raster of an area at a coarse resolution

Methods for changing the resolution of a raster

The two principal ways to determine resulting values when changing the resolution of a raster dataset are interpolation and aggregation.

Interpolation

The interpolation method is used by the Resample tool in the Raster toolset of the Data Management toolbox. It uses either the nearest neighbor, bilinear, cubic interpolation, or majority resampling methods on the values of the input raster.

Aggregation

The aggregation method uses a specified statistical aggregation method within a neighborhood to derive values in the output raster at the different resolution. This method is used by the Aggregate and Block Statistics tools.

  • Aggregate—This tool works by aggregating the individual values of a group of cells and producing a single, coarser resolution cell of that value. The types of statistics available to aggregate the input values are Sum, Min, Max, Mean, and Median.
  • Block Statistics—This tool works by calculating a specified statistic on the input cells within non-overlapping neighborhoods.

The main difference between them is that there is no concept of a neighborhood in Aggregate as there is in Block Statistics, since the would-be neighborhood and output blocks are always square, and the size of the would-be neighborhood is a function of the aggregation of cells that is necessary to obtain the desired resolution.

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