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Pan-sharpening

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 datasets

Use in combination with Processing from Storage.

How it works

ParameterOverview
methodstring / required
A method used in the pansharpening procedure. The allowed values:
  • SFIM
  • Brovey
  • Esri
The default value is SFIM.
edge_sharpen_factorfloat
Available if method="SFIM".
Factor to reduce blurring of edges in a pansharpened result.
weightfloat
Available if method="Brovey".
Can be set to a value between 0 and 1. The default value is 0.2.
weightsarray of floats
Available if method="Esri".
The weights in sequence for each multispectral bands that depend on the overlap of the spectral sensitivity curves of the multispectral bands with the panchromatic band. For Pleiades the default weights are [0.2, 0.34, 0.34, 0.23] while for SPOT weights are [0.24, 0.2, 0.24, 0].
bboxarray 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.
containsobject / 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.
intersectsobject / 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_aoiboolean / required
Whether the specified AOI should be clipped before processing:
  • true — The AOI should be clipped first.
  • false — The full scene should be processed.
The default value is false.
include_panboolean / required
If true, includes the panchromatic band in the output pansharpened image. The default value is false.

Synthetic panchromatic band Sentinel-2

Sentinel-2 provides a high range of multispectral bands with different spatial resolutions (10, 20 and 60 m). Since there is no panchromatic (PAN) band in Sentinel-2 images, we use a synthetic panchromatic band 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. Read more about this process in the paper by [Kaplan2018]2.

Methods

In [Vivone2014]3 an extensive review of pansharpening procedures was performed, with results being assessed on the geometric detail of the final result and additionally the spectral correspondence of the pansharpened result with the input multispectral imagery.

In this paper, SFIM, or Smoothing Filter-based Intensity Modulation (based on [Liu2000]1, has one of the top performances in all of the metrics assessed and because of this we have selected this method as the default pansharpening procedure.

Additionally, two other methods have been implemented, Brovey or Weighted Brovey and Esri, as described below.

  • SFIM

    SFIM has been developed based on a simplified solar radiation and land surface reflection model.

    By using a ratio between a higher resolution image (panchromatic band) and its low pass filtered (with a smoothing filter) image, spatial details can be modulated to a lower resolution multispectral image without altering its spectral properties and contrast.

    An additional parameter has been added to control the blurred edges that appear in the pansharpened result (edge_sharpen_factor) — setting this factor to 1.7 (the default) removes most of this effect. Read more about this procedure in the paper from [Liu2000]1.

    {
      "pansharpen:1": {
        "edge_sharpen_factor": 1.7
      }
    }
  • Brovey

    The Brovey transformation is based on spectral modeling and was developed to increase the visual contrast in the high and low ends of the data's histogram.

    It uses a method that multiplies each resampled, multispectral pixel by the ratio of the corresponding panchromatic pixel intensity to the sum of all the multispectral intensities. It assumes that the spectral range spanned by the panchromatic image is the same as that covered by the multispectral channels.

    {
      "pansharpen:1": {
        "method": "Brovey",
        "weight": 0.2
      }
    }
  • Esri

    The Esri pansharpening transformation uses a weighted average to create its pansharpened output bands.

    The result of the weighted average is used to create an adjustment value that is then used in calculating the output values. The weights for the multispectral bands depend on the overlap of the spectral sensitivity curves of the multispectral bands with the panchromatic band. 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.

    Don't use the Esri pansharpening method with Sentinel-2 data.

    • Pleiades weights

      {
        "pansharpen:1": {
          "method": "Esri",
          "weights": [0.2, 0.34, 0.34, 0.23]
        }
      }
    • SPOT weights

      {
        "pansharpen:1": {
          "method": "Esri",
          "weights": [0.24, 0.2, 0.24, 0]
        }
      }

Processing

Additional local interpolation of outlier values in the panchromatic bands of Pleiades and Spot data ensures a consistent pansharpened multispectral image.

Example

An example using the SPOT 6/7 Reflectance (Download) as a data source, returning the pansharpened multispectral product appended with the panchromatic band.

{
  "oneatlas-spot-fullscene:1": {
    "ids": null,
    "bbox": [
      13.405215963721279,
      52.48480326228838,
      13.4388092905283,
      52.505278605259086
    ],
    "time": null,
    "limit": 1,
    "order_ids": null,
    "time_series": null
  },
  "pansharpen:1": {
    "include_pan":true
    "bbox":null
    "contains": null,
    "intersects": null,
    "clip_to_aoi": false,
  }
}

An example using the SPOT 6/7 Reflectance (Download) as a data source, returning the pansharpened multispectral product appended with the panchromatic band which is clipped to the specific AOI.

{
  "oneatlas-spot-fullscene:1": {
    "ids": null,
    "bbox": [
      13.415594100952148,
      52.491560852691116,
      13.430356979370117,
      52.49992172845934
    ],
    "time": null,
    "limit": 1,
    "order_ids": null,
    "time_series": null
  },
  "pansharpen:1": {
    "include_pan":true
    "bbox": [
      13.415594100952148,
      52.491560852691116,
      13.430356979370117,
      52.49992172845934
    ],
    "contains": null,
    "intersects": null,
    "clip_to_aoi": true,
  }
}

An example using the ESA Sentinel-2 L2A Analytic (GeoTIFF) as a data source, returning the pansharpened multispectral product with 13 bands (including the panchromatic band) which is clipped to the specific AOI.

{
  "esa-s2-l2a-gtiff-analytic:1": {
    "ids": null,
    "bbox": [13.415594100952148, 52.491560852691116, 13.430356979370117, 52.49992172845934],
    "time": null,
    "limit": 1,
    "order_ids": null,
    "time_series": null
  },
  "pansharpen:1": {
    "include_pan": true,
    "bbox": [13.415594100952148, 52.491560852691116, 13.430356979370117, 52.49992172845934],
    "contains": null,
    "intersects": null,
    "clip_to_aoi": true
  }
}

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)
formatGTiff
sensor> (propagated)
resolution> (propagated)
processing_level> (propagated)
To know more please check the block capabilities specifications.

Learn more


  1. Kaplan, G., Avdan, U. (2018). Sentinel-2 Pan Sharpening—Comparative Analysis. Proceedings 2(345).
  2. Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G. A. & Wald, L. (2014). A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2565-2586.
  3. Liu, J. G. (2000). Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461-3472.