Fundamentals of panchromatic sharpening

Panchromatic sharpening uses a higher-resolution panchromatic image (or raster band) to fuse with a lower-resolution multiband raster dataset. The result produces a multiband raster dataset with the resolution of the panchromatic raster where the two rasters fully overlap.

Panchromatic sharpening is a radiometric transformation available through the user interface or from a geoprocessing tool. Several image companies provide low-resolution, multiband images and higher-resolution, panchromatic images of the same scenes. Panchromatic sharpening is used to increase the spatial resolution and provide a better visualization of a multiband image using the high-resolution, single-band image.

Examples using panchromatic sharpening

ArcGIS provides four image fusion methods from which to choose to create the pan-sharpened image: the Brovey transformation, the IHS transformation, the ESRI pan-sharpening transformation, and the simple mean transformation. Each of these methods uses different models to improve the spatial resolution while maintaining the color, and some are adjusted to include a weighting so that a fourth band can be included (such as the near-infrared band available in many multispectral image sources). By adding the weighting and enabling the infrared component, the visual quality in the output colors is improved.

The Brovey transformation is based on spectral modeling and was developed to increase the visual contrast in the high and low ends of data's histogram. It uses a method that multiplies each resampled, multispectral pixel by the ratio of the corresponding panchromatic pixel intensity to the sum of all the multispectral intensities. It assumes that the spectral range spanned by the panchromatic image is the same as that covered by the multispectral channels.

In the Brovey transformation, the general equation uses red, green, and blue (RGB) and the panchromatic bands as inputs to output new red, green, and blue bands. For example:

Red_out = Red_in / [(blue_in + green_in + red_in) * Pan]

However, by using weights and the near-infrared band (when available), the adjusted equation for each band becomes

DNF = (P - IW * I) / (RW * R + GW * G + BW * B)
Red_out = R * DNF
Green_out = G * DNF
Blue = B * DNF
Infrared_out = I * DNF

where the inputs are

P = panchromatic image
R = red band
G = green band
B = blue band
I = near infrared
W = weight

The IHS transformation is a transformation of RGB and intensity, hue, and saturation. Each coordinate is represented by a 3D coordinate position within the color cube. Pixels having equal components of red, green, and blue are on the gray line, a line from the cube to the opposite corner (Lillesand and Kiefer 2000). Hue is the actual color; it describes the shade of the color and where that color is found in the color spectrum. Blue, orange, red, and brown are words that describe hue. Saturation describes the value of lightness (or whiteness) measured in percent from 0 to 100 percent. For example, when mixing red with a saturation of 0 percent, it will be as red as it can be. As the saturation percentage increases, more white is added and the red will change to pink. If the saturation is 100 percent, the hue is meaningless (essentially, red loses its color and turns to white). Intensity describes a value of brightness based on the amount of light emanating from the color. A dark red has less intensity than a bright red. If the intensity is 0 percent, the hue and saturation are meaningless (essentially, the color is lost and becomes black).

The IHS transformation converts the color image from an RGB color model to an IHS color model. It replaces the intensity values with those obtained from the panchromatic image being used to sharpen the image; a weighting value; and the value from an optional, near-infrared band. The resultant image is output using the RGB color mode. The equation used to derive the altered intensity value is as follows:

Intensity = P - I * IW

The ESRI pan-sharpening transformation uses a weighted averaging and the additional near-infrared band (optional) to create its pan-sharpened output bands. The result of the weighted average is used to create an adjustment value (ADJ) that is then used in calculating the output values. For example:

ADJ = pan image - WA
Red_out = R + ADJ
Green_out = G + ADJ
Blue_out = B + ADJ
Near_Infrared_out = I + ADJ

For the ESRI pan-sharpening transformation, the weight values of 0.166, 0.167, 0.167, and 0.5 (R, G, B, I) provide good results when using QuickBird imagery. By changing the near-infrared weight value, the green output can be made more or less vibrant.

The simple mean transformation method applies a simple mean averaging equation to each of the output band combinations. For example:

Red_out= 0.5 * (Red_in + Pan_in)
Green_out = 0.5 * (Green_in + Pan_in)
Blue_out= 0.5 * (Blue_in + Pan_in)

In all cases, the sum of the weights given to each band should equal 1; however, if they do not, the algorithm will make the necessary adjustments when calculating the transformation to each band.

How to pan-sharpen

To apply the panchromatic sharpening technique to a multiband raster dataset in ArcMap, use the RGB Composite renderer on the Symbology tab or use the Pan-sharpen button Pan-sharpening on the Image Analysis window.

To create a raster dataset as a result of pan sharpening, use the Create Pan-sharpened Raster Dataset tool, or after creating one in ArcMap, you can export the layer to a raster dataset.

Related Topics