Pan sharpening

Pan Sharpening
NADIA AHMED AZIZ

INTRODUCTION
Pan sharpening is a process of merging high-resolution panchromatic and
lower resolution multispectral imagery to create a single high-resolution color
image.
A multispectral image contains a higher degree of spectral resolution than a
panchromatic image, while often a panchromatic image will have a higher
spatial resolution than a multispectral image. A pan sharpened image represents
a sensor fusion between the multispectral and panchromatic images which gives
the best of both image types, high spectral resolution AND high spatial
resolution. This is the simple why of pan sharpening.

Panchromatic sharpening is one of the most used techniques in remote sensing
imaginary. Google Maps and mostly nearly every map creating company use
this technique to increase image quality. Further, this Sharpened image can be
used in various application and to extract important features from image data
such as area calculation

fundamental concepts
Multispectral Data
A multispectral image is an image that contains more than one spectral band.
It is formed by a sensor which is capable of separating light reflected from the
earth into discrete spectral bands. A color image is a very simple example of a
multispectral image that contains three bands. In this case, the bands
correspond to the blue, green and red wavelength bands of the
electromagnetic spectrum. The electromagnetic spectrum is the wavelength
(or frequency) mapping of electromagnetic energy, as shown in the figure.

Panchromatic data
In contrast to the multispectral image, a panchromatic image contains
only one wide band of reflectance data. The data is usually representative
of a range of bands and wavelengths, such as visible or thermal infrared,
that is, it combines many colors so it is “pan” chromatic.
Panchromatic images can generally be collected with higher spatial
resolution than a multispectral image because the broad spectral range
allows smaller detectors to be used while maintaining a high signal to
noise ratio.

Panchromatic sharpening methods
ArcGIS provides five image fusion methods from which to choose to create
the pan-sharpened image:
1. The Brovey transformation.
2. The intensity-hue-saturation (IHS) transformation.
3. The Esri pan-sharpening transformation.
4. The simple mean transformation.
5. 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.

1. Brovey
The Brovey transformation is based on spectral modeling and was
developed to increase the visual contrast in the high and low ends of
the data's histogram.
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]

2. Esri
The Esri pan-sharpening transformation uses a weighted average and the additional near-infrared band
(optional).
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 weights for the multispectral bands depend on the overlap of the spectral sensitivity curves of the
multispectral bands with the panchromatic band.

3. 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, and 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 vector.

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

IHS
The IHS pan-sharpening method converts the multispectral image from RGB
to intensity, hue, and saturation. The low-resolution intensity is replaced with
the high-resolution panchromatic image. If the multispectral image contains an
infrared band, it is taken into account by subtracting it using a weighting factor.
The equation used to derive the altered intensity value is as follows:
Intensity = P - I * IW
Then the image is back-transformed from IHS to RGB in the higher resolution.

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)

Case study
Pan sharpening Image Using ArcGIS
(Landsat 8)

Landsat 8
Landsat 8 carries two push-broom instruments: The Operational Land
Imager (OLI) and the Thermal Infrared Sensor (TIRS).
images consist of eight spectral bands with a spatial resolution of 30 meters
for Bands 1 to 7 and 9.
The resolution for Band 8 (panchromatic) is 15 meters. Thermal bands 10
and 11 are useful in providing more accurate surface temperatures and are
collected at 100 meters.

Landsat 8
Operational
Land Imager
(OLI)
and
Thermal
Infrared
Sensor
(TIRS)
Launched
February 11, 2013
Bands
Wavelength
(micrometers)
Resolution
(meters)
Band 1 - Coastal aerosol 0.43 - 0.45 30
Band 2 - Blue 0.45 - 0.51 30
Band 3 - Green 0.53 - 0.59 30
Band 4 - Red 0.64 - 0.67 30
Band 5 - Near Infrared (NIR) 0.85 - 0.88 30
Band 6 - SWIR 1 1.57 - 1.65 30
Band 7 - SWIR 2 2.11 - 2.29 30
Band 8 - Panchromatic 0.50 - 0.68 15
Band 9 - Cirrus 1.36 - 1.38 30
Band 10 - Thermal Infrared
(TIRS) 1
10.60 - 11.19 100
Band 11 - Thermal Infrared
(TIRS) 2
11.50 - 12.51 100

Displayed below are some common band combinations in RGB comparisons
for Landsat 7 or Landsat 5, and Landsat 8.
Landsat 7
Landsat 5
Landsat 8
Color Infrared: 4, 3, 2 5,4,3
Natural Color: 3, 2, 1 4,3,2
False Color: 5,4,3 6,5,4
Landsat 5
Landsat 8
Color Infrared: 4, 3, 2 5,4,3
Natural Color: 3, 2, 1 4,3,2

Applying pan-sharpening to a raster layer
in ArcMap:
1. In ArcMap, add the lower-resolution color image to the map using the Add
Data button.
2. Right-click the raster layer in the table of contents and click Properties.
3. Click the Symbology tab.
4. Click the Panchromatic Image drop-down arrow and click an image name
or click the browse button and select the higher-resolution image.

5. Click the Pan-sharpening Type drop-down list and choose the desired color transformation.
• IHS
• Brovey
• Esri
• Simple Mean
• Gram-Schmidt
6. Optionally, type a weight value for each of the red, green, blue, and infrared bands.
7. Optionally, if the fourth band of your raster dataset is the infrared band and you want to use it, then
you need to check the 4th-band as Infrared Image check box.

Pan sharpening

More Related Content

What's hot (20)

Similar to Pan sharpening (20)

Recently uploaded (20)

Pan sharpening