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Gram-Schmidt Pan Sharpening

Gram-Schmidt Pan Sharpening

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), please use the NNDiffuse or SPEAR pan sharpening tools.

You can also write a script to perform pan sharpening using the ENVIGramSchmidtPanSharpeningTask routine.

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 panochromatic 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.
  2. Select the low-resolution multispectral input file, perform optional spatial and spectral subsetting and/or masking, then click OK.
  3. Select the high-resolution input band, then click OK.
  4. From the Sensor drop-down list, select the sensor that the image was acquired from. If the sensor is not listed here, select Unknown.
  5. From the Resampling drop-down list, select a method: Nearest Neighbor, Bilinear (default), or Cubic Convolution.
  6. From the Output Format drop-down list, select ENVI or TIFF image format.
  7. Select an output filename and folder.
  8. Click OK. ENVI adds the resulting output to the Layer Manager.



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