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Statistics function

Overview

The Statistics function calculates focal statistics for each pixel of an image, based on a defined focal neighborhood.

Notes

The table below provides examples of the types using two different neighborhood dimensions based on the following image:

Unfiltered image

Statistics Type3x3 Neighborhood5x5 Neighborhood

Minimum

Calculates the minimum value of the pixels within the neighborhood.

Min 3x3

Min 5x5

Maximum

Calculates the maximum value of the pixels within the neighborhood.

Max 3x3

Max 5x5

Mean

Calculates the average value of the pixels within the neighborhood. This is the default.

Mean 3x3

Mean 5x5

Standard Deviation

Calculates the standard deviation value of the pixels within the neighborhood.

Std 3x3

Std 5x5

Focal statistical functions

The Neighborhood Settings allow you to enter the Number of Rows and the Number of Columns to use as your neighborhood dimensions.

Parameters

ParameterDescription

Raster

The input raster to perform focal statistics upon.

Statistics Type

There are four types of focal statistical functions:

  • Minimum—Calculates the minimum value of the pixels within the neighborhood
  • Maximum—Calculates the maximum value of the pixels within the neighborhood
  • Mean—Calculates the average value of the pixels within the neighborhood. This is the default.
  • Standard Deviation—Calculates the standard deviation value of the pixels within the neighborhood

Number of Rows

The number of pixel rows to use in your focal neighborhood dimension.

Number of Columns

The number of pixel columns to use in your focal neighborhood dimension.

Fill NoData Pixels Only

Check this option to fill in NoData gaps in your output. This is very useful when there might be dropped lines in your imagery.

Learn more about filling in dropped lines

The Statistics function can be used to fill dropped lines in an image. Dropped lines are often caused by problems in the sensor where data is not collected. This has happened in sensors such as Landsat 7's Enhanced Thematic Mapper Plus (ETM+). This missing data causes problems for analysis and also when looking at the imagery. There is little that can be done when using the imagery for analysis; however, if there was an overlapping image, it could be used in place of the missing content. The same could be done if the imagery is being used for visualization. However, there isn't always an extra image to fill in the missing content, so it must be derived from the existing data.

This process requires two functions. First, insert the Mask function to convert the dropped line pixel values to NoData. For example, if the values are 0, then on the Mask function type 0 for each band in the NoData Values column. Next, insert the Statistics function. Use the Mean type, define the number of rows and columns to use for the neighborhood, and check Fill NoData pixel values only.

Dropped lines
Dropped lines
Filled dropped lines
Filled dropped lines

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