Panchromatic sharpening (short to pan sharpening) is a technique used in GIS and remote sensing. to enhance the spatial resolution of multispectral images by combining them with higher-resolution panchromatic images. The resulting image retains the information of multispectral data and with the increased resolution of panchromatic data.
What is pan sharpening?
Pan sharpening (also written as pan-sharpening) merges a high-resolution panchromatic (black-and-white) image with lower-resolution multispectral images. Panchromatic images provide detailed spatial information but lack color, while multispectral images capture data across multiple wavelengths, such as red, green, blue, and near-infrared, but at lower spatial resolution. Combining these datasets produces an image that is both spectrally rich and spatially sharp.
Landsat imagery and pan sharpening
Landsat satellites began offering a panchromatic band with the launch of Landsat 7 in 1999 which makes data from this longest running Earth observation suite of satellites a popular choice for pan sharpening.
On Landsat 8 and Landsat 9, the panchromatic band provides a spatial resolution of 15 meters, compared to the 30-meter resolution of the multispectral bands. The panchromatic band captures light across a broad spectrum, offering finer spatial detail, while the multispectral bands focus on specific wavelengths for detailed spectral data. This difference allows for pan sharpening, combining the strengths of both datasets to create imagery that is both spectrally and spatially detailed.
The nine spectral bands of Landsat 8 and Landsat 9 are:
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- Band 1 Coastal Aerosol (0.43 – 0.45 µm) 30 m
- Band 2 Blue (0.450 – 0.51 µm) 30 m
- Band 3 Green (0.53 – 0.59 µm) 30 m
- Band 4 Red (0.64 – 0.67 µm) 30 m
- Band 5 Near-Infrared (0.85 – 0.88 µm) 30 m
- Band 6 SWIR 1(1.57 – 1.65 µm) 30 m
- Band 7 SWIR 2 (2.11 – 2.29 µm) 30 m
- Band 8 Panchromatic (PAN) (0.50 – 0.68 µm) 15 m
- Band 9 Cirrus (1.36 – 1.38 µm) 30 m
How does pan sharpening work?
Pan sharpening involves several key steps.
First, the panchromatic and multispectral images are geometrically aligned to ensure that corresponding pixels represent the same geographic area. Next, image fusion algorithms are applied to combine the spectral data of the multispectral image with the spatial detail of the panchromatic image.
Some commonly used methods include:
- Intensity-Hue-Saturation (IHS): Converts the multispectral image to IHS color space, replaces the intensity component with the panchromatic image, and converts it back to the original color space.
- Principal Component Analysis (PCA): Transforms the multispectral image into principal components, integrates the panchromatic data, and converts it back.
- Brovey Transformation: Emphasizes brightness and contrast by scaling spectral bands using the panchromatic image.
- Wavelet Transform: Uses wavelet decomposition to combine images at different resolutions.
- HSV Sharpening: Similar to IHS sharpening, HSV sharpening converts the image into a hue-saturation-value (HSV) color space. The value (brightness) component is replaced with the high-resolution panchromatic image, and the image is then converted back to its original RGB format. This method works particularly well with imagery like Landsat, preserving spectral integrity while enhancing spatial detail.
Once the images are merged, the resulting pan-sharpened image is fine-tuned to reduce distortions and ensure accurate representation.
Benefits and limitations of pan sharpening
Pan sharpening offers significant advantages for visualizing and analyzing geographic data but comes with some challenges.
Benefits:
- Improved visualization: Pan-sharpened images provide a more detailed and clearer view of geographic features.
- Enhanced analysis: High-resolution imagery allows for more precise feature extraction and classification.
- Efficient data use: Merges the strengths of existing datasets for greater analytical power.
Limitations:
- Spectral distortion: Some methods may alter the spectral characteristics of multispectral data, potentially impacting analysis accuracy.
- Computational intensity: Processing large datasets for pan sharpening can be resource-intensive.
- Algorithm sensitivity: The choice of algorithm and the quality of input data significantly influence the results.
Advances in pan sharpening techniques
Recent research has introduced advanced methods to improve pan sharpening outcomes:
- Deep Convolutional Sparse Coding Network: This approach uses side information to guide the fusion process, enhancing the quality of pan-sharpened images. arXiv
- Cross Modulation Transformer (CMT): CMT employs a modulation technique within the attention mechanism to dynamically adjust weights, effectively integrating spatial and spectral attributes. A hybrid loss function combining Fourier and wavelet transforms further enhances spatial and spectral accuracy. arXiv
Performing pan sharpening in GIS software
Many GIS platforms, such as ArcGIS, QGIS, and Google Earth Engine, include tools for pan sharpening. The typical workflow involves loading the panchromatic and multispectral images, selecting a fusion algorithm, running the process, and validating the results. For example, ArcGIS Pro offers a pan sharpening tool within its “Image Analyst” extension, allowing users to customize algorithms and parameters to suit their specific needs.
Pan sharpening:
GIS tutorials for pan sharpening satellite imagery
- Pan Sharpen Landsat Imagery in QGIS
- Landsat 9 + QGIS | Band Combination and Pan sharpening
- Pan sharpen Landsat 8 image using Google Earth Engine
- Pan sharpening in ArcGIS Pro
- Landsat 9 Band Combination and Pansharpening with Blend Modes – ArcGIS Pro
Pan sharpening can also be done using image editing software. NASA’s Earth Observatory blog provides a guide on how to use Photoshop to apply HSV sharpening to Landsat 8 imagery.
