Pansharpen
Pansharpens images from Pléiades / SPOT Reflectance (Download) or Sentinel-2 L2A Analytic (GeoTIFF).
Description
A processing block that creates a single high resolution color image from high resolution panchromatic and lower resolution multispectral image bands.
See this block on the marketplace.
Compatibility
Data blocks
Catalog collections
Use in combination with Processing from Storage.
How it works
Pansharpening is a fusion process that combines a high resolution panchromatic band with a multispectral band. The result of the process is a high resolution image with preserved spectral characteristics. Pansharpening can be useful before applying certain segmentation or classification algorithms.
With Pléiades and SPOT collections
The process uses panchromatic and multispectral bands acquired at the same time by the same sensor, so the result image retains the high spatial resolution of the panchromatic image and the spectral information from the multispectral image.
With Sentinel-2 data block
There is no panchromatic band in Sentinel-2 images, so a synthetic panchromatic band is used to increase the spatial resolution of the 20 m and 60 m bands to 10 m. The synthetic panchromatic band is generated using the average value of the visual and the near-infrared bands.
Parameter | Overview |
---|---|
method | string / required The method used in the pansharpening procedure. The allowed values:
SFIM . |
edge_sharpen_factor | float Use only if method="SFIM" Factor to reduce blurring of edges in a pansharpened result. The default value is 1.7 . |
weight | float Use only if method="Brovey" The multiplication value that lets you modulate the influence of multispectral values on the final image. Can be set to a value between 0 and 1 . The default value is 0.2 . |
weights | array of floats Use only if method="Esri" The sequence of weights for each multispectral band that depend on the overlap of the spectral sensitivity curves of the multispectral bands with the panchromatic band. The order of the values in the array: R, G, B, NIR. The multispectral band with the largest overlap with the panchromatic band should get the largest weight. A multispectral band that doesn't overlap at all with the panchromatic band should get a weight of 0 . By changing the near-infrared weight value, the green output can be made more or less vibrant.
|
bbox | array of integers / required if Required if intersects or contains aren't specified.A bounding box to use as an AOI. Will clip to scenes that intersect with this box. |
contains | object / required if Required if bbox or intersects aren't specified.A GeoJSON geometry to use as an AOI. Will clip to scenes that fully cover this geometry. |
intersects | object / required if Required if bbox or contains aren't specified.A GeoJSON geometry to use as an AOI. Will clip to scenes that intersect with this geometry. |
clip_to_aoi | boolean / required Whether the specified AOI should be clipped before processing:
false . |
include_pan | boolean / required If true , includes the panchromatic band in the output pansharpened image. The default value is false . |
How to use it in a workflow
- Open the console, go
Projects, and select a project.
- Go to the Workflows tab and click Create Workflow.
- Name your workflow.
- Add Sentinel-2 L2A Analytic (GeoTIFF) or Processing from Storage as a data block.
- Add Pansharpen as a processing block.
- Click Save & Configure Job.
- Adjust job parameters as follows:
- If you use Sentinel-2 data as a data source, configure a job accordingly.
- If you use Pléiades or SPOT assets from storage, find an asset ID and use it in a job.
- Choose a pansharpening method and configure its parameters.
- Click Run job.
After that you can download job outputs and visualize them.
Examples
Click to see an example using SFIM
{
"up42-processing-from-storage:1": {
"asset_ids": ["01f4fb18-dfd4-49b6-aba0-8853cb494809"]
},
"pansharpen:1": {
"method": "SFIM",
"edge_sharpen_factor": 1.7
"bbox": null,
"contains": null,
"intersects": null,
"clip_to_aoi": false,
"include_pan": false
}
}
Click to see an example using Brovey
{
"up42-processing-from-storage:1": {
"asset_ids": ["01f4fb18-dfd4-49b6-aba0-8853cb494809"]
},
"pansharpen:1": {
"method": "Brovey",
"weight": 0.2
"bbox": null,
"contains": null,
"intersects": null,
"clip_to_aoi": false,
"include_pan": false
}
}
Click to see an example using Esri
{
"up42-processing-from-storage:1": {
"asset_ids": ["01f4fb18-dfd4-49b6-aba0-8853cb494809"]
},
"pansharpen:1": {
"method": "Esri",
"weights": [0.2, 0.34, 0.34, 0.23]
"bbox": null,
"contains": null,
"intersects": null,
"clip_to_aoi": false,
"include_pan": false
}
}
Capabilities
Input
raster
up42_standard | |
---|---|
bands | {
"or": [
[
"coastal",
"blue",
"green",
"red",
"rededge",
"rededge2",
"rededge3",
"nir",
"nir2",
"watervapour",
"swir2",
"swir3"
],
[
"red",
"green",
"blue",
"nir",
"pan"
]
]
} |
format | {
"or": [
"DIMAP",
"MTL"
]
} |
sensor | {
"or": [
"Pleiades",
"SPOT",
"Sentinel2"
]
} |
Output
raster
up42_standard | |
---|---|
bands | {
"or": [
[
"red",
"green",
"blue",
"nir"
],
[
"red",
"green",
"blue",
"nir",
"pan"
],
[
"coastal",
"blue",
"green",
"red",
"rededge",
"rededge2",
"rededge3",
"nir",
"nir2",
"watervapour",
"swir2",
"swir3"
],
[
"coastal",
"blue",
"green",
"red",
"rededge",
"rededge2",
"rededge3",
"nir",
"nir2",
"watervapour",
"swir2",
"swir3",
"pan"
]
]
} |
dtype | > (propagated) |
format | GTiff |
sensor | > (propagated) |
resolution | > (propagated) |
processing_level | > (propagated) |
Learn more
- How pansharpening improves satellite imagery
- Fundamentals of panchromatic sharpening
- Sentinel-2 Pan Sharpening—Comparative Analysis
- A Critical Comparison Among Pansharpening Algorithms
- Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details
- Remote sensing image processing 101
- Influence of the weights in IHS and Brovey methods for pan-sharpening WorldView-3 satellite images