| Label | Explanation | Data Type |
Input Raster | The raster dataset with the spatial resolution to be changed. | Mosaic Dataset; Mosaic Layer; Raster Dataset; Raster Layer |
Output Raster Dataset | The name, location, and format of the dataset being created.
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 |
Output Cell Size (Optional) | The cell size of the new raster using an existing raster dataset or by specifying its width (x) and height (y). | Cell Size XY |
Resampling Technique (Optional) | Specifies the resampling technique to be used.
| String |
Summary
Changes the spatial resolution of a raster dataset and sets rules for aggregating or interpolating values across the new pixel sizes.
Usage
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; the Majority option 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. If this is unacceptable, use Bilinear instead. The output from cubic convolution 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.
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 sizeIf 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.
Parameters
arcpy.management.Resample(in_raster, out_raster, {cell_size}, {resampling_type})| Name | Explanation | Data Type |
in_raster | The raster dataset with the spatial resolution to be changed. | Mosaic Dataset; Mosaic Layer; Raster Dataset; Raster Layer |
out_raster | The name, location, and format of the dataset being created.
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 |
cell_size (Optional) | 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:
| Cell Size XY |
resampling_type (Optional) |
Specifies the resampling technique to be used.
| String |
Code sample
This is a Python sample for the Resample function.
import arcpy
arcpy.Resample_management("c:/data/image.tif", "resample.tif", "10 20", "NEAREST")This is a Python script sample for the Resample function.
# Resample TIFF image to a higher resolution
import arcpy
arcpy.env.workspace = r"C:/Workspace"
arcpy.Resample_management("image.tif", "resample.tif", "10", "CUBIC")Environments
Licensing information
- Basic: Yes
- Standard: Yes
- Advanced: Yes