With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one.
The resolution merge or pan sharpening is the technique used to obtain high resolution multi-spectral images. The color information is collected from the coarse resolution satellite data and the intensity from the high resolution satellite data.
The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset.
The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
2. With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has
deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For
most of the systems, panchromatic images typically have higher resolution, while multispectral images
offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to
combine advantages of both kinds of images by merging them into one.
The resolution merge or pan sharpening is the technique used to obtain high resolution multi-spectral
images. The color information is collected from the coarse resolution satellite data and the intensity
from the high resolution satellite data.
The main constraint is to preserve the spectral information for aspects like land use. Saving
theimage from distortion of the spectral characteristics is important in the merged dataset.
The most common techniques for spatial enhancement of low-resolution imagery combining high
and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component,
Multiplicative and Brovey Transform.
3. 1. Run the app. Right click
on the image list on the
content tab to add an
image. Go to Open
Raster Layer. (Ctrl+O)
2. Browse the file and
load it. Load both the
images.
STEPS
4. As you can see the resolution (1;1) of panchromatic image on the
right is better and crispier than the multi-spectral left one.
5. 3. Go to Raster after the
image has been loaded
and click on Pan Sharpen.
4. Select Modified HIS
Resolution Merge.
6. 5. In the dialog box of Modified HIS Resolution Merge make the required settings as demonstrated.
7. 6. Set the output file destination and click
OKAY.
7. Load the new merged image
alongside the panchromatic image.
8. Notice the new enhanced
resolution of the multispectral
image.
9. 7. Select Resolution Merger from Pan
Sharpen in Raster tab.
8. The settings for Principal Component Merge.
16. Merging methods for utilising both the high resolution panchromatic and the multispectral images in a
combined manner is a way of improving the resolution and to achieve a optimal spatial detail
augmentation and a minimal color distortion. The results of demonstrate that IHS produces vibrant
colour in the imagery while Principal Component and Brovey Transform techniques achieve significantly a
closer spatial characteristic detail with the soft colour transformation which makes the photo
interpretation easier.
Intensity-Hue-Saturation method works by assessing the spectral overlap between each multispectral
band and the high resolution panchromatic band and weighting the merge based on these relative
wavelengths. Therefore, it works best when merging images (and bands) where there is significant
overlap of the wavelengths.
The biggest limitation of a method based on IHS processing is that it can only process three
bands at a time (because of using the RGB to IHS method) discouraging its use in application of
classification. However, it can be over-ruled by running multiple passes of the algorithm and merging
the resulting layers.
17. Principal Component re-maps the high resolution image into the data range of first principal
component and substitutes it, then applies an inverse principal components transformation. The
method serves best when the original scene radiometry (color balance) of the input multispectral
image is to be maintained as closely as possible in the output file.
However, sharpness is a bit on the downward side.
Multiplicative method applies a simple multiplicative algorithm which integrates the two raster
images. This is the simplest of the four methods. As it is computationally simple it is generally the
fastest method and requires the least system resources. However, the resulting merged image does
not retain the radiometry of the input multispectral image. Instead, the intensity component is
increased, making this technique good for highlighting urban features as evident in the images.
Brovey Transform uses a ratio algorithm to combine the images. It visually increases the contrast in
the low and high ends of an images histogram to provide contrast in shadows, water and high
reflectance areas such as urban features as evident in the image samples.
Consequently, it should not be used if preserving the original scene radiometry is important.
However, it is good for producing RGB images with a higher degree of contrast in the low and
high ends of the image histogram and for producing visually appealing images.
