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Symbolizing a single-band raster using the Classified method requires a histogram to be built on the raster dataset. However, for a large-sized raster that has small cell sizes, for example, 30 x 30 cells, building a histogram for the entire raster dataset can be time-consuming.
The example below is a normalized difference vegetation index (NDVI) layer with the pixel size of approximately 3.28 feet. To symbolize the NDVI layer using the Classified method, a histogram must be built. However, given the fine resolution and large spatial extent, scanning through all the pixel values of the original large raster dataset to calculate histogram may take a significant amount of time. Hence, a quicker way to estimate and build the histogram is to resample the raster to a raster with reduced sampling size, build the histogram on the resampled raster, and import the histogram to the original raster.
Note: Using the resampling technique, the resampled raster does not represent the original data distribution completely. However, this method is a way to approximate the original distribution with a smaller sample size to estimate the histogram more quickly.
The steps provided describe how to resample a large raster to a reduced resolution raster using the Resample Function, import the resampled raster's histogram to the original raster dataset using the Statistics and Histograms Function, and symbolizing the raster layer with Classified symbology.
Note: To view the imported histogram and symbolize the layer using the Classified method, open the Symbology window of the new layer generated in Step 9.