Use Gram-Schmidt Pan Sharpening to sharpen multispectral data using high spatial resolution data.

The source images must be georeferenced to a standard map projection. If the images have different projections, ENVI reprojects the low-resolution image before performing the sharpening. For RPC-based images (for example, Pleiades and WorldView-2), use the NNDiffuse tools.

You can also write a script to perform pan sharpening using the GramSchmidtPanSharpening task.

Pan-sharpening algorithms are used to sharpen multispectral data using high spatial resolution panchromatic data. An underlying assumption of these algorithms is that you can accurately estimate what the panchromatic data would look like using lower spatial resolution multispectral data.

The Gram-Schmidt and PC spectral sharpening tools both create pan-sharpened images, but using different techniques. Generally speaking, the Gram-Schmidt method is more accurate than the PC method and is recommended for most applications. Gram-Schmidt is typically more accurate because it uses the spectral response function of a given sensor to estimate what the panchromatic data look like.

If you display a Gram-Schmidt pan-sharpened image and a PC pan-sharpened image, the visual differences are very subtle. The differences are in the spectral information; compare a Z Profile of the original image with that of the pan-sharpened image to see the differences in spectral information, or calculate a covariance matrix for both images. The effect of pan sharpening is best revealed in images with homogenous surface features (flat deserts or water, for example).

The low spatial resolution spectral bands to use to simulate the panchromatic band must fall in the range of the high spatial resolution panchromatic band or they will not be included in the resampling process.

ENVI performs Gram-Schmidt spectral sharpening by:

  1. Simulating a panchromatic band from the lower spatial resolution spectral bands.
  2. Performing a Gram-Schmidt transformation on the simulated panchromatic band and the spectral bands, using the simulated panchromatic band as the first band.
  3. Swapping the high spatial resolution panchromatic band with the first Gram-Schmidt band.
  4. Applying the inverse Gram-Schmidt transform to form the pan-sharpened spectral bands.

Reference

Laben et al., Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening, US Patent 6,011,875.

Note: Ensure that you have adequate disk space before performing a Gram-Schmidt transformation, because this process creates an output file and several temporary files. An error message will appear during the process if you do not have adequate disk space.

To apply Gram-Schmidt spectral sharpening:

  1. From the Toolbox, select Image Sharpening > Gram-Schmidt Pan Sharpening. The Gram-Schmidt Pan Sharpening dialog appears.
  2. In the Input Low Resolution Raster field, select a low spatial resolution multispectral input file. Perform optional spatial and spectral subsetting, and/or masking, then click OK.
  3. In the Input High Resolution Raster field, select a high-resolution input image. Perform any optional spatial subsetting, then click OK.
  4. In the Sensor field, specify a lower-case string for the sensor that acquired the high-resolution panchromatic input. If a sensor is not specified, then ENVI determines if the data has valid sensor information in the metadata. If not, the sensor is set to Unknown. For a list of valid sensor strings, see the <INSTALL_DIR>\resource\filterfuncs directory. User-defined sensors are valid as long as they are in this directory.
  5. From the Resampling drop-down list, select a method: Nearest Neighbor, Bilinear (default), or Cubic Convolution.
  6. Enter a filename and location for the Output Raster.

  7. Enable the Preview check box to see a preview of the settings before you click OK to process the data. The preview is calculated only on the area in the view and uses the resolution level at which you are viewing the image. See Preview for details on the results. To preview a different area in your image, pan and zoom to the area of interest and re-enable the Preview option.

  8. Enable the Display result check box to display the output in the view when processing is complete. Otherwise, if the check box is disabled, the result can be loaded from the Data Manager.

  9. To reuse these task settings in future ENVI sessions, save them to a file. Click the down arrow and select Save Parameter Values, then specify the location and filename to save to. Note that some parameter types, such as rasters, vectors, and ROIs, will not be saved with the file. To apply the saved task settings, click the down arrow and select Restore Parameter Values, then select the file where you previously stored your settings.

  10. To run the process in the background, click the down arrow and select Run Task in the Background. If an ENVI Server has been set up on the network, the Run Task on remote ENVI Server name is also available. The ENVI Server Job Console will show the progress of the job and will provide a link to display the result when processing is complete. See ENVI Servers for more information.

  11. Click OK.