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.

ArcGIS provides five image fusion methods from which to choose to create the pan-sharpened image: the Brovey transformation, the intensity-hue-saturation (IHS) transformation, the Esri pan-sharpening transformation, the simple mean transformation, and the Gram-Schmidt spectral sharpening method. 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.

Brovey

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_out = 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

Esri

The Esri pan-sharpening transformation uses a weighted average 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

The same weights can be used for the Esri or Gram-Schmidt methods. The weights for the multispectral bands depend on the overlap of the spectral sensitivity curves of the multispectral bands with the panchromatic band. The weights are relative and will be normalized when they are used. The multispectral band with the largest overlap with the panchromatic band should get the largest weight. A multispectral band that does not overlap at all with the panchromatic band should get a weight of 0. By changing the near-infrared weight value, the green output can be made more or less vibrant.

Some suggested weights for common sensors are (order: red, green, blue, infrared):

• GeoEye—0.6, 0.85, 0.75, 0.3
• IKONOS—0.85, 0.65, 0.35, 0.9
• QuickBird—0.85, 0.7, 0.35, 1.0
• WorldView–2—0.95, 0.7, 0.5, 1.0

Gram-Schmidt

The Gram-Schmidt pan-sharpening method is based on a general algorithm for vector orthogonalization—the Gram-Schmidt orthogonalization. This algorithm takes in vectors (for example, 3 vectors in 3D space) that are not orthogonal, then rotates them so that they are orthogonal afterward. In the case of images, each band (panchromatic, red, green, blue, and infrared) corresponds to one high-dimensional vector (#dimensions = #pixels).

In the IHS pan-sharpening method, the multispectral bands are decorrelated by transforming them into IHS space. The low-resolution intensity band gets replaced by the high-resolution pan band, and the result is back-transformed in high resolution to get the high-resolution multispectral (MS) bands.

In the Gram-Schmidt pan-sharpening method, the first step is to create a low-resolution pan band by computing a weighted average of the MS bands. Next, these bands are decorrelated using the Gram-Schmidt orthogonalization algorithm, treating each band as one multidimensional vector. The simulated low-resolution pan band is used as the first vector; which is not rotated or transformed. The low-resolution pan band is then replaced by the high-resolution pan band, and all bands are back-transformed in high resolution.

The same band weights suggested for the Esri method can also be used by the Gram-Schmidt method.

The details are described in the following patent:

Laben, Craig A., and Bernard V. Brower. Process for Enhancing the Spatial Resolution of Multispectral Imagery using Pan-Sharpening. U.S. Patent 6,011,875, filed April 29, 1998, and issued January 4, 2000. Eastman Kodak Company, Rochester, N.Y.

IHS

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

Simple mean

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)

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 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.