Pansharpening function

Available with Image Server

The Pansharpening function uses a higher-resolution panchromatic image or raster band to fuse with a lower-resolution, multiband imagery layer to increase the spatial resolution of the multiband image.

To learn about pan sharpening, see Fundamentals of panchromatic sharpening.

Notes

The purpose of pan sharpening is to create a higher quality visual image. Since the techniques alter the radiometry and spectral characteristics of the multiband imagery, pan sharpened imagery needs to be used with caution for analytical remote sensing purposes.

The weights used for each band are relative, and will be normalized when they are used.

Parameters

Parameters	Description
Multispectral	The multispectral raster dataset that you want to sharpen using the panchromatic band.
Panchromatic	The high-resolution, single-band raster dataset that will be used to pan sharpen the lower-resolution multispectral raster.
Pansharpening Type	Choose the pan sharpening algorithm you want to use. Brovey—Uses the Brovey algorithm based on spectral modeling for data fusion. Esri—Uses the Esri algorithm based on spectral modeling for data fusion. Gram-Schmidt—Uses the Gram-Schmidt spectral-sharpening algorithm to sharpen multispectral data. IHS—Uses Intensity, Hue, and Saturation color space for data fusion. Mean—Uses the averaged value between the red, green, and blue values and the panchromatic pixel value.
Sensor	When the Gram-Schmidt algorithm is chosen, you can also specify the sensor that collected the multiband raster input. Choosing the sensor type will set appropriate band weights.
Red-Band Weight	Specify the weight for the red band. The value should be within the range of 0 to 1.
Green-Band Weight	Specify the weight for the green band. The value should be within the range of 0 to 1.
Blue-Band Weight	Specify the weight for the blue band. The value should be within the range of 0 to 1.
IR-Band Weight	Specify the weight for the infrared band. The value should be within the range of 0 to 1.