Ella Walter
Pan sharpening is an image fusion technique that combines a high-resolution panchromatic image with a lower-resolution multispectral image to create a single, high-resolution colour image. This process is used to increase the spatial resolution and provide better visualisation of a multiband image using a high-resolution, single-band image. The result is a multiband raster dataset with the resolution of the panchromatic raster. Google Maps and most map-creating companies use this technique to increase image quality.
| Characteristics | Values |
|---|---|
| Definition | A technique that combines a high-resolution panchromatic image with a lower-resolution multiband image to create a single, high-resolution colour image |
| Other Names | Panchromatic sharpening, image fusion technique |
| Result | A multiband raster dataset with the resolution of the panchromatic raster |
| Use Cases | To increase image quality and spatial resolution, commonly used by map-creating companies like Google Maps |
| Image Fusion Methods | Brovey transformation, Esri pan sharpening transformation, Gram-Schmidt spectral sharpening method, IHS transformation, simple mean transformation |
| Common Colour-Space Transformations | HSI (hue-saturation-intensity), YCbCr |
| Resolution Trade-Off | Pansharpening can only improve one of either spatial or spectral resolution, not both |
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What You'll Learn
- Pan sharpening uses a higher-resolution panchromatic image
- It fuses with a lower-resolution multiband raster dataset
- The result is a multiband raster dataset with the resolution of the panchromatic raster
- Pan sharpening is a radiometric transformation
- It is available through a raster function or geoprocessing tool
Pan sharpening uses a higher-resolution panchromatic image
Pan sharpening is a technique that uses a higher-resolution panchromatic image (or raster band) to merge with a lower-resolution multiband raster dataset. The result of this process is a multiband raster dataset with the resolution of the panchromatic raster. This technique is used to increase the spatial resolution and improve the visualization of a multiband image by leveraging the high-resolution characteristics of the single-band image.
The panchromatic band captures a much wider range of light, allowing it to achieve twice the level of detail compared to individual spectral bands. This increased level of detail is highly valuable as it enables the identification of features or phenomena that would otherwise remain unseen.
Several image fusion methods can be employed to create the pan-sharpened image, including the Brovey transformation, Esri pan sharpening transformation, Gram-Schmidt spectral sharpening method, intensity, hue, saturation (IHS) transformation, and simple mean transformation. The Gram-Schmidt method, for instance, involves creating a low-resolution pan band by computing a weighted average of the MS bands, followed by decorrelating these bands using the Gram-Schmidt orthogonalization algorithm. Subsequently, the low-resolution pan band is replaced by the high-resolution pan band, and all bands are back-transformed into high resolution.
Pan sharpening is widely applied by map-creating companies, including Google Maps, to enhance image quality. It is also used in satellite data sets, such as Landsat 7, which combines six 30-meter resolution multispectral bands, a 60-meter thermal infrared band, and a 15-meter resolution panchromatic band. By pansharpening the Landsat 8 multispectral imagery, the spatial resolution can be improved from 30 meters to 15 meters.
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It fuses with a lower-resolution multiband raster dataset
Pan sharpening is an image fusion technique that combines a high-resolution panchromatic image with a lower-resolution multiband raster dataset. This process produces a multiband raster dataset with the resolution of the high-resolution panchromatic raster. Pan sharpening is a radiometric transformation that can be achieved through a raster function or a geoprocessing tool. It is used to increase the spatial resolution and improve the visualisation of a multiband image by utilising the high-resolution capabilities of the panchromatic image.
The panchromatic image, often referred to as a pan band, captures a wide spectrum of light, resulting in a sharper and more detailed image. This high-resolution panchromatic image is then fused with the lower-resolution multiband raster dataset, typically containing colour information from other bands, usually only the visible bands. By combining the detailed spatial information of the panchromatic image with the colour data of the lower-resolution dataset, pan sharpening creates a high-resolution colour image.
Several image fusion methods can be employed to create the pan-sharpened image. One such method is the Gram-Schmidt pan-sharpening technique, which is commonly used in GIS software. This method involves creating a low-resolution pan band by computing a weighted average of the multispectral bands. The bands are then decorrelated using the Gram-Schmidt orthogonalization algorithm, treating each band as a multidimensional vector. Subsequently, the low-resolution pan band is replaced by the high-resolution pan band, and all bands are back-transformed to high resolution.
Another image fusion technique is the Esri pan-sharpening transformation, which utilises a weighted average and an additional near-infrared band (optional) to create its pan-sharpened output bands. The result of the weighted average is used to derive an adjustment value (ADJ), which is then employed in calculating the output values. The weights for the multispectral bands are relative and are normalised when applied. The multispectral band with the most significant overlap with the panchromatic band receives the largest weight, while a band with no overlap should be assigned a weight of zero.
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The result is a multiband raster dataset with the resolution of the panchromatic raster
The goal of pan sharpening is to merge a high-resolution panchromatic image with a lower-resolution multiband raster dataset to create a single, enhanced image. The process involves using a higher-resolution panchromatic image, often referred to as a raster band, and fusing it with a lower-resolution multiband raster dataset. This results in a multiband raster dataset that adopts the high resolution of the panchromatic raster.
This technique is particularly useful when dealing with satellite imagery, where multispectral and panchromatic images are often captured separately. By combining these images through pan sharpening, the resulting dataset benefits from the high spatial resolution of the panchromatic image and the detailed spectral information of the multiband image.
The process of pan sharpening involves several methods, including the Brovey transformation, Gram-Schmidt spectral sharpening, and intensity, hue, saturation (IHS) transformation. These methods aim to balance and fuse the spectral information from the multiband image with the high-resolution detail of the panchromatic image.
One specific approach, the Gram-Schmidt pan-sharpening method, involves creating a low-resolution pan band by computing a weighted average of the multiband image's spectral bands. The Gram-Schmidt orthogonalization algorithm is then applied to decorrelate these bands, treating each band as a multidimensional vector. Subsequently, the low-resolution pan band is replaced by the high-resolution pan band, and all bands are back-transformed to high resolution.
The outcome of pan sharpening is a significant improvement in image quality, making it a valuable technique for applications such as Google Maps and other mapping services. It enhances the visualization of multiband images, providing sharper details and better overall clarity.
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Pan sharpening is a radiometric transformation
The pan-sharpening process involves merging high-resolution panchromatic and lower-resolution multispectral imagery to create a single high-resolution colour image. This is achieved by using a higher-resolution panchromatic image (or raster band) to fuse with a lower-resolution multiband raster dataset, resulting in a multiband raster dataset with the resolution of the panchromatic raster. This technique is used by Google Maps and nearly every map-creating company to increase image quality.
There are five image fusion methods to create the pan-sharpened image: the Brovey transformation, the Esri pan-sharpening transformation, the Gram-Schmidt spectral sharpening method, the intensity, hue, saturation (IHS) transformation, and the simple mean transformation. Each of these methods uses different models to improve spatial resolution while maintaining colour. Some methods, such as the IHS transformation, convert the multispectral image from RGB to intensity, hue, and saturation.
The Esri pan-sharpening transformation uses a weighted average and an additional near-infrared band 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. The weights for the multispectral bands depend on the overlap of the spectral sensitivity curves with the panchromatic band, with the band having the largest overlap getting the largest weight.
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It is available through a raster function or geoprocessing tool
Pan sharpening is a technique that combines a high-resolution panchromatic image with a lower-resolution multiband raster dataset to create a single high-resolution colour image. It is a radiometric transformation available through a raster function or a geoprocessing tool.
The process involves merging a high-resolution panchromatic image with a lower-resolution multispectral image to enhance the spatial resolution and visualisation of the final image. The panchromatic image, often in grayscale, captures a wide range of light, while the multispectral image covers narrow portions of the visible, near-infrared, and shortwave infrared wavelengths. By fusing these images, the panchromatic details sharpen the multispectral imagery while preserving its spectral information.
Several image fusion methods can be employed for pan sharpening, including the Brovey transformation, Esri pan sharpening transformation, Gram-Schmidt spectral sharpening method, intensity, hue, saturation (IHS) transformation, and simple mean transformation. The choice of method depends on the specific requirements and characteristics of the images being combined.
To perform pan sharpening, software tools such as ERDAS IMAGINE or image editors like Adobe Photoshop and GIMP are used. These tools allow users to work with high-bit data, combine and edit colour channels, and support advanced colour spaces. By following tutorials and fine-tuning parameters, users can effectively apply pan sharpening techniques to enhance the quality and detail of their images.
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Frequently asked questions
Pan sharpening is a technique that combines a high-resolution panchromatic image with a lower-resolution multiband raster dataset to create a single high-resolution colour image.
The first step in pan sharpening is to acquire the necessary data, i.e. both the multispectral (lower resolution) and panchromatic (higher resolution) images. The bands are then layer-stacked and combined using one of five image fusion methods: Brovey transformation, Esri pan sharpening transformation, Gram-Schmidt spectral sharpening method, intensity, hue, saturation (IHS) transformation, or simple mean transformation.
Pan sharpening is commonly used by map-creating companies like Google Maps to increase image quality. It is also used in GIS software and for enhancing satellite imagery, such as that from Landsat 7 and 8.
