4.2.2 Pan-sharpening

Because of physical constrains on the sensor design, it is difficult to achieve high spatial and spectral resolution at the same time : a better spatial resolution means a smaller detector, which in turns means lesser optical flow on the detector surface. On the contrary, spectral bands are obtained through filters applied on the detector surface, that lowers the optical flow, so that it is necessary to increase the detector size to achieve an acceptable signal to noise ratio.

For these reasons, many high resolution satellite payload are composed of two sets of detectors, which in turns delivers two different kind of images :

It is very frequent that those two images are delivered side by side by data providers. Such a dataset is called a bundle. A very common remote sensing processing is to fuse the panchromatic image with the multi-spectral one so as to get an image combining the spatial resolution of the panchromatic image with the spectral richness of the multi-spectral image. This operation is called pan-sharpening.

This fusion operation requires two different steps :

  1. The multi-spectral (XS) image is zoomed and registered to the panchromatic image,
  2. A pixel-by-pixel fusion operator is applied to the co-registered pixels of the multi-spectral and panchromatic image to obtain the fused pixels.

Using either OTB Applications or modules from Monteverdi, it is possible to perform both steps in a row, or step-by-step fusion, as described in the above sections.

Pan-sharpening with OTB Applications

The BundleToPerfectSensor application allows to perform both steps in a row. Seamless sensor modelling is used to perform zooming and registration of the multi-spectral image on the panchromatic image. In the case of a Pléiades bundle, a different approach is used : an affine transform is used to zoom the multi-spectral image and apply a residual translation. This translation is computed based on metadata about the geometric processing of the bundle. This zooming and registration of the multi-spectral image over the panchromatic image can also be performed by the Superimpose application.

After the registration step, a simple pan-sharpening is applied, according to the following formula:

PXS(i,j)= --PAN-(i,j)--⋅XS(i,j)
         PANsmooth(i,j)
(4.1)

Where i and j are pixels indices, PAN is the panchromatic image, XS is the multi-spectral image and PANsmooth is the panchromatic image smoothed with a kernel to fit the multi-spectral image scale.

Here is a simple example of how to use the BundleToPerfectSensor application:

otbcli_BundleToPerfectSensor -inp pan_image -inxs xs_image -out output_image

There are also optional parameters that can be useful for this tool:

Pan-sharpening is a quite heavy processing requiring a lot of system resource. The -ram option allows you to limit the amount of memory available for the computation, and to avoid overloading your computer. Increasing the available amount of RAM may also result in better computation time, seems it optimises the use of the system resources. Default value is 256 Mb.

Pan-sharpening with Monteverdi

Monteverdi allows to perform step-by-step fusion. The followings screenshots highlight operations needed to perform Pan-Sharpening.

Please also note that since registration and zooming of the multi-spectral image with the panchromatic image relies on sensor modelling, this tool will work only for images whose sensor models is available in Orfeo ToolBox (see section 4.2.4 for a detailed list). It will also work with ortho-ready products in cartographic projection.