Resample (Data Management)


Change the spatial resolution of your raster dataset and set rules for aggregating or interpolating values across the new pixel sizes.


  • The cell size can be changed, but the extent of the raster dataset will remain the same.

  • You can save your output to BIL, BIP, BMP, BSQ, DAT, Esri Grid , GIF, IMG, JPEG, JPEG 2000, PNG, TIFF, MRF, CRF, or any geodatabase raster dataset.

  • The Output Cell Size parameter can resample the output to the same cell size as an existing raster layer, or it can output a specific X and Y cell size.

  • There are four options for the Resampling Technique parameter:

    • Nearest—Performs a nearest neighbor assignment and is the fastest of the interpolation methods. It is used primarily for discrete data, such as a land-use classification, since it will not change the values of the cells. The maximum spatial error will be one-half the cell size.
    • Majority—Performs a majority algorithm and determines the new value of the cell based on the most popular values in the filter window. It is mainly used with discrete data just as the nearest neighbor method; Majority tends to give a smoother result than Nearest. The majority resampling method will find corresponding 4 by 4 cells in the input space that are closest to the center of the output cell and use the majority of the 4 by 4 neighbors.
    • Bilinear—Performs a bilinear interpolation and determines the new value of a cell based on a weighted distance average of the four nearest input cell centers. It is useful for continuous data and will cause some smoothing of the data.
    • Cubic—Performs a cubic convolution and determines the new value of a cell based on fitting a smooth curve through the 16 nearest input cell centers. It is appropriate for continuous data, although it may result in the output raster containing values outside the range of the input raster. It is geometrically less distorted than the raster achieved by running the nearest neighbor resampling algorithm. The disadvantage of the Cubic option is that it requires more processing time. In some cases, it can result in output cell values outside the range of input cell values. If this is unacceptable, use Bilinear instead.

    The Bilinear and Cubic options should not be used with categorical data, since the cell values may be altered.

  • If the center of the pixel in output space falls exactly the same as one of the pixels in the input cells, that particular cell value gets all the weights, causing the output pixel to be the same as the cell center. This will affect the result of bilinear interpolation and cubic convolution.

  • The lower left corner of the output raster dataset will be the same map space coordinate location as the lower left corner of the input raster dataset.

  • The number of rows and columns in the output raster are determined as follows:

    columns = (xmax - xmin) / cell size rows = (ymax - ymin) / cell size

  • If there is any remainder from the above equations, rounding of the number of columns and rows is performed.

  • This tool supports multidimensional raster data. To run the tool on each slice in the multidimensional raster and generate a multidimensional raster output, be sure to save the output to CRF.

    Supported input multidimensional dataset types include multidimensional raster layer, mosaic dataset, image service, and CRF.


Resample(in_raster, out_raster, {cell_size}, {resampling_type})
ParameterExplanationData Type

The raster dataset with the spatial resolution to be changed.

Mosaic Dataset; Mosaic Layer; Raster Dataset; Raster Layer

The name, location, and format of the dataset being created.

  • .bilEsri BIL
  • .bipEsri BIP
  • .bmp—BMP
  • .bsqEsri BSQ
  • .dat—ENVI DAT
  • .gif—GIF
  • .jpg—JPEG
  • .jp2—JPEG 2000
  • .png—PNG
  • .tif—TIFF
  • .mrf—MRF
  • .crf—CRF
  • No extension for Esri Grid

When storing a raster dataset in a geodatabase, do not add a file extension to the name of the raster dataset. When storing a raster dataset to JPEG, JPEG 2000, or TIFF format, or in a geodatabase, you can specify a compression type and compression quality.

Raster Dataset

The cell size of the new raster using an existing raster dataset or by specifying its width (x) and height (y).

You can specify the cell size in the following ways:

  • Use a single number specifying a square cell size.
  • Use two numbers that specify the x and y cell size, which is space delimited.
  • Use the path of a raster dataset from which the square cell size will be imported.

Cell Size XY

Specifies the resampling technique to be used.

  • NEAREST Nearest neighbor is the fastest resampling method; it minimizes changes to pixel values since no new values are created. It is suitable for discrete data, such as land cover.
  • BILINEAR Bilinear interpolation calculates the value of each pixel by averaging (weighted for distance) the values of the surrounding four pixels. It is suitable for continuous data.
  • CUBIC Cubic convolution calculates the value of each pixel by fitting a smooth curve based on the surrounding 16 pixels. This produces the smoothest image but can create values outside of the range found in the source data. It is suitable for continuous data.
  • MAJORITYMajority resampling determines the value of each pixel based on the most popular value in a 3 by 3 window. Suitable for discrete data.

Code sample

Resample example 1 (Python window)

This is a Python sample for the Resample tool.

import arcpy
arcpy.Resample_management("c:/data/image.tif", "resample.tif", "10 20", "NEAREST")
Resample example 2 (stand-alone script)

This is a Python script sample for the Resample tool.

# Resample TIFF image to a higher resolution

import arcpy
arcpy.env.workspace = r"C:/Workspace"
arcpy.Resample_management("image.tif", "resample.tif", "10", "CUBIC")

Licensing information

  • Basic: Yes
  • Standard: Yes
  • Advanced: Yes

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