What is Pansharpening?
The goal of pansharpening in remote sensing is to achieve the highest level of visual clarity and detail from an image.
By combining the high spatial resolution of panchromatic band imagery and the wide spectral range of multispectral (color) imagery, pansharpening produces a final color image with sharper quality.
This guide to pansharpening in remote sensing will cover the advantages and disadvantages of pansharpening and some tips for success. It will also provide an overview of the software you need and discuss the applications of pansharpening in research and industry.
Overview of Pansharpening
A panchromatic (black and white) band is imagery with high spatial resolution and low spectral resolution. Whereas multispectral imagery has low spatial resolution and high spectral resolution.
By combining these two images, you can create a final image that has both high spatial and spectral resolution, allowing for a more detailed representation of the earth’s surface.
The process of pansharpening involves applying a mathematical algorithm so that the cells of the panchromatic image can enhance the spatial resolution information in the cells of the multispectral image.
The result of pansharpening is an image that has the high resolution of a panchromatic image. But it still contains the color information of the multispectral image. This makes it easier to identify objects in a scene since features are both easier to see and have color information associated with them.
Advantages and Disadvantages of Pansharpening
Here are some of the main advantages of pansharpening:
1. Increased detail: By combining the high spatial resolution of PAN imagery with the spectral information of MS imagery, pansharpening produces an output with both high spectral and spatial resolution. This allows for a more detailed analysis than either imagery type could provide on its own.
2. Improved visualization: With the increased detail of the pansharpened image, users can more easily identify objects and features within the image. This can be useful for tasks such as mapping, where it is important to distinguish individual objects within the image.
3. Easier classification: The greater detail and color information of the pansharpened image allows for more accurate identification of objects and features within the image, making it easier to assign them to specific classes.
4. Inconsistent spectral values: A disadvantage of pansharpening is that it is difficult to maintain the spectral integrity of the data. For instance, it’s generally not recommended to perform an NDVI analysis on a post-processed pansharpened image.
Imagery for Pansharpening
As long as there is a panchromatic band, then it’s possible to perform pansharpening. So if you want to use pansharpening, then you have to know which satellite imagery contains a panchromatic band. Here are some examples of remote sensing data with a panchromatic band:
For Landsat-8, the panchromatic band 8 with a spatial resolution of 15m. This is half the pixel size of visible red, green, and blue bands, which are 30m. For more information, here is a breakdown of the Landsat band combinations.
Worldview-2 – Worldview-2 contains 9 spectral bands. The coastal aerosol, blue, green, yellow, red, red edge, and NIR bands are 1.85m in resolution. The 0.45m panchromatic band provides an opportunity to sharpen the imagery more so through pansharpening with Worldview imagery.
SPOT-7 – Although SPOT-7 contains just 5 spectral bands, one of them is the panchromatic band with a GSD of 1.5m. The other 4 bands consist of blue, green, red, and near-infrared at 6m pixel size.
There are a number of different methods and GIS software available for pansharpening. Some software packages designed for pansharpening include:
ArcGIS Pro – Once you have the correct images, you can add them both to your ArcGIS Pro project and then use the Image Analysis Window’s Pansharpening tool to combine them. The Pansharpening tool in ArcGIS Pro will allow you to select the type of algorithm you want to use to combine the images, such as Brovey, Simple Mean, or IHS.
QGIS 3 – If you’re using QGIS 3, pansharpening is available in the default toolbox. First, you can use the pansharpening tool from the GRASS toolbox, which gives you the option for Brovey, IHS, and PCA. Alternatively, you can use the pansharpening tool in the GDAL toolbox. Although you don’t get as many options with the GDAL pansharpening, you can set the bit depth and resampling method.
ERDAS Imagine – ERDAS Imagine is a powerful remote sensing and image analysis software package developed by Hexagon Geospatial. Pansharpening is part of the ERDAS Imagine core set of raster tools. If you want to perform pansharpening, locate the “Pansharpening” tool within the “Raster” tab. From here, it’s a matter of going through the wizard and setting your inputs and outputs.
Applications in Research and Industry
Pansharpening is commonly used in remote sensing research and commercial applications where high-resolution images are necessary. Because the resolution is high, you typically find it in web map applications like Google Earth or Apple Maps.
Environmental Sciences: In the environmental sciences, pansharpened imagery can help monitor land cover change, assess water resources, and identify vegetation types.
Urban Planning: In urban planning, pansharpened images are used to locate buildings, roads, and other infrastructure, as well as to map out population centers and development trends.
Agriculture: In agriculture, pansharpened images can monitor crop growth and health, and other farming needs.
Military and Reconnaissance: Pansharpened imagery is also used in the military for reconnaissance and surveillance, as well as for terrain mapping and target identification.
Commercial: Finally, pansharpened imagery is also increasingly being used in the field of geospatial analytics. Companies are using this type of imagery to better understand their customers, identify new market opportunities, and gain deeper insights into their business operations.
Summary: Pansharpening in Remote Sensing
Pansharpened imagery combines both panchromatic (black and white) and multispectral (color) imagery to create a higher-resolution, more detailed image.
We use this type of imagery in a variety of applications, from environmental sciences to urban planning and agriculture.
Do you use pansharpening? What are some of the challenges and benefits? Please let us know with a comment below.