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PAN-Sharp service specifications


This service performs Pan Sharpening of optical multispectral images. It takes as input a calibrated dataset containing multispectral assets and a calibrated dataset containing a panchromatic asset to generate a pansharpened image by combining the panchromatic and multispectral data.

đź“• The tutorial of the PAN-Sharp service is available in this section.

Service Description

The Optical Pan Sharpened Image Generation (PAN-Sharp) service performs Pan Sharpening of optical remote sensing multispectral images. It generates high resolution “Pan Sharpened” images by combining panchromatic and multispectral data.

Pansharpened Multispectral images are widely employed in damage assessment to enhance image visualization, interpretation, and extraction of features. As an example, Pan-sharpened true colour composites are derived only from source EO data having the following input assets: Panchromatic band (PAN) and Multispectral bands (XS) for red, green and blue CBN.

Several methods for the fusion of multispectral and panchromatic images are available in literature (Loncan et al., 20151; Mhangara et al., 20202; De BĂ©thune et al., 19983; Fasbender et al., 20084). In the ESA Charter Mapper, the fusion of multispectral and panchromatic images is performed by using the OTB Pansharpening toolset5. In this processor it is possible to employ the Ratio Component Substitution (RCS) method. Pansharpened MS bands Products are available in the ESA Charter Mapper for calibrated Datasets.


Based on the input optical calibrated products having both PAN and XS assets, the PAN-Sharp workflow is:

graph TB c(cos2) --> d[Dataset] d[Dataset] --> B((Optical<br>calibration)) B((Optical<br>calibration)) --> A[input] subgraph Inputs A aoi[AOI] end subgraph Optical Pan Sharpened Image Generation A[input]-->c1{has<br>PAN?} A[input]-->c2{has<br>XS?} c1--yes--> D((PAN-Sharp)) c2--yes--> D((PAN-Sharp)) D -.-> e{is AOI set?} e --yes-.-> sp((spatial<br>subset)) aoi -.-> sp end subgraph Output sp -.-> os1[overview-ps-trc subset] sp -.-> os2[r-ps-coastal subset] sp -.-> os3[r-ps-blue subset] sp -.-> os4[etc. for each input CBN subset] e --no-.-> o1[overview-ps-trc] e --no-.-> o2[r-ps-coastal] e --no-.-> o3[r-ps-blue] e --no-.-> o4[etc. for each input CBN] end


This service supports Optical EO data from different sensors.


Input optical EO data must be a calibrated optical dataset having both pan and at least red, green and blue multi-spectral assets.


This Pan Sharpening processor cannot be employed with thermal bands (lwir CBNs).


The PAN-Sharp service requires a specified number of mandatory and optional parameters. All service parameters are listed in the below Table 1.

Parameter Description Required Default value
Input-reference-multi-spectral Optical calibrated Dataset with Multispectrals Assets YES
Input-reference-pan Optical calibrated Dataset with a Panchromatic Asset YES
Area of Interest Area of interest expressed in WKT NO

Table 1 - Service parameters for the PAN-Sharp processor.

Input product reference for multi spectral assets

The first parameter represents the reference to the input optical calibrated dataset that is used to gather multispectral assets (e.g. blue, green, red, nir).

Input product reference for pan-chromatic assets

The second parameter represents the reference to the input optical calibrated dataset that is used to gather the panchromatic asset pan.


The firts two parameters are mandatory. Therefore, an Optical Calibrated Dataset having only pan asset cannot be used as input for PAN-Sharp.


In case panchromatic and multispectral assets are included in the same Optical Calibrated Dataset (e.g. Landsat-8 OLI) Input-reference-multi-spectral and Input-reference-pan parameters values are identical and pointing to the same reference.

AOI (optional)

This third parameter (optional) may define the area of interest expressed as a Well-Known Text value.


In the definition of “Area of interest as Well Known Text” it is possible to apply as AOI the drawn polygon defined with the area filter. To do so, click on the Magic tool wizard button in the left side of the "Area of interest expressed as Well-known text" box and select the option AOI from the list. The platform will automatically fill the parameter value with the rectangular bounding box taken from current search area in WKT format.


The outputs of the Optical Pan Sharpened Image Generation service are the following products given in COG format: 1. TOA/BOA reflectance for each pan sharpened multispectral band, 2. TRC overview from pan sharpened multispectral bands.

The output of PAN-Sharp is a STAC Item with the output Assets defined as shown in the table below:

Attribute Value / description
Long Name TRC RGB composite from calibrated Pan-Sharpened Multispectral Optical data
Short Name overview-ps-trc
Description True Color RGBA band composite from Pan-Sharpened MS data (it includes alpha band)
Processing level L1 / L2 (according to input)
Data Type UnSigned 8-bit Integer
Band 4
Format COG
Projection Native
Units Dinensionless
Valid Range [1 - 255]
Fill Value 0
Attribute Value / description
Long Name TOA reflectance from Pan-Sharpened Optical EO data
Short Name r-ps-coastal, r-ps-blue, etc.
Description Pan-Sharpened Top Of Atmosphere (TOA) or Bottom Of Atmosphere (BOA) reflectance from XS bands as multi-band GeoTIFF
Processing level L1 / L2 (according to input)
Data Type Unsigned 16-bit Integer
Band 1 (single band for each pan sharpened CBN)
Format COG
Projection Native
Units Dinensionless
Valid Range [0 - 10,000]
Scale Factor *0.0001

  1. L. Loncan et al. (2015), "Hyperspectral Pansharpening: A Review," in IEEE Geoscience and Remote Sensing Magazine, vol. 3, no. 3, pp. 27-46, Sept. 2015. DOI: 10.1109/MGRS.2015.2440094. ↩

  2. Mhangara P., Mapurisa W., Mudau N. (2020) Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery. Appl. Sci., 10, 1881. DOI: [10.3390/app10051881] ({:target="blank"}. ↩

  3. Fasbender D., Radoux J., Bogaert P. (2008), “Bayesian Data Fusion for Adaptable Image Pansharpening”, Geoscience and Remote Sensing, IEEE Transactions on. 46. 1847 - 1857. DOI: 10.1109/TGRS.2008.917131. ↩

  4. De Béthune S., Muller F., Donnay J. P. (1998), “Fusion of multispectral and panchromatic images by local mean and variance matching filtering techniques”, in Proceedings of the Second International Conference on Fusion of Earth Data, Sophia Antipolis, France, 28–30 January 1998; pp. 31–36. Available at: ↩

  5. Orfeo Toolbox CookBook Pansharpening Available at:"blank"}. ↩