From raw image to calibrated product¶

Optical calibration with OTB Applications¶

The OpticalCalibration application allows to perform optical calibration. The mandatory parameters are the input and output images. All other parameters are optional. By default the level of calibration is set to TOA (Top Of Atmosphere). The output images are expressed in thousandth of reflectivity using a 16 bits unsigned integer type.

A basic TOA calibration task can be performed with the following command:

otbcli_OpticalCalibration -in  input_image -out output_image

A basic TOC calibration task can be performed with the following command:

otbcli_OpticalCalibration -in  input_image -out output_image -level toc

Optical calibration with Monteverdi¶

These transformations can also be done in Monteverdi .

The 6S model needs atmospheric parameters to be able to compute radiative terms to estimate the atmospheric contributions on the input signal. Default parameters are available in the module. For atmospheric parameters, it is possible to indicate AERONET file. The AERONET (AErosol RObotic NETwork) program is a federation of ground-based remote sensing aerosol networks established by NASA and PHOTONS (Univ. of Lille 1, CNES, and CNRS-INSU) and is greatly expanded by collaborators from national agencies, institutes, universities, individual scientists, and partners. The program provides accessible public domain database of aerosol optical, mircrophysical and radiative properties.

The module produces four outputs:

• Luminance image.
• TOA reflectance image.
• TOC reflectance image.
• Difference TOA-TOC image, which allows to get the estimation of atmospheric contribution.

Figure 1 : Optical calibration module.

Figure 2 : Optical calibration module’s outputs.

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:

Where i and j are pixels indices, PAN is the panchromatic image, XS is the multi-spectral image and PAN_{smooth} 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:

• The -elev option allows to specify the elevation, either with a DEM formatted for OTB (-elev.dem option, see section [ssec:dem]) or with an average elevation (-elev.default option). Since registration and zooming of the multi-spectral image is performed using sensor-models, it may happen that the registration is not perfect in case of landscape with high elevation variation. Using a DEM in this case allows to get better registration.
• The -lmSpacing option allows to specify the step of the registration grid between the multi-spectral image and panchromatic image. This is expressed in amount of panchromatic pixels. A lower value gives a more precise registration but implies more computation with the sensor models, and thus increase the computation time. Default value is 10 pixels, which gives sufficient precision in most of the cases.
• The -mode option allows to select the registration mode for the multi-spectral image. The default mode uses the sensor model of each image to create a generic “MS to Pan” transform. The phr mode uses a simple affine transform (which doesn’t need an elevation source nor a registration grid).

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.

• Open panchromatic and multispectral images in monteverdi using the Open Dataset module or using the -il option of the Monteverdi executable.
• The Superimpose module is used to zoomed and registered the multispectral on the panchromatic image. As a result, we get a multispectral dataset with the same geographic extension and the same resolution as the panchromatic image, cf  [fig:qbmulsuper].

Figure 4 : Panchromatic, Zoomed, and registered multispectral image.

• Now the Simple RCS pan-sharpening module can be used using the panchromatic and the multispectral images as inputs. It produces a multispectral image with the same resolution and geographic extension (cf Figure 5).

Figure 5 : Pan-sharpened image using the simple RCS module.

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 Ortho-rectification and map projections for a detailed list). It will also work with ortho-ready products in cartographic projection.

Beware of “ortho-ready” products¶

There are some image products, called “ortho-ready”, that should be processed carefully. They are actual products in raw geometry, but their metadata also contains projection data :

• a map projection
• a physical origin
• a physical spacing
• and sometimes an orientation angle

The purpose of this projection information is to give an approximate map projection to a raw product. It allows you to display the raw image in a GIS viewer at the (almost) right location, without having to reproject it. Obviously, this map projection is not as accurate as the sensor parameters of the raw geometry. In addition, the impact of the elevation model can’t be observed if the map projection is used. In order to perform an ortho-rectification on this type of product, the map projection has to be hidden from Orfeo Toolbox .

You can see if a product is an “ortho-ready” product by using tools such as gdalinfo or ReadImageInfo, and check if the product verifies the 2 following conditions :

• The product is in raw geometry : you should expect the presence of RPC coefficients and a non-empty OSSIM keywordlist.
• The product has a map projection : you should see a projection name with physical origin and spacing.

In that case, you can hide the map projection from the Orfeo Toolbox by using extended filenames. Instead of using the plain input image path, you append a specific key at the end :

"path_to_image?&skipcarto=true"

The double quote can be necessary for a successful parsing. More details about the extended filenames can be found in the wiki page , and also in the OTB Software Guide .

Ortho-rectification with OTB Applications¶

The OrthoRectification application allows to perform ortho-rectification and map re-projection. The simplest way to use it is the following command:

otbcli_OrthoRectification -io.in input_image -io.out output_image

In this case, the tool will automatically estimates all the necessary parameters:

• The map projection is set to UTM (a worldwide map projection) and the UTM zone is automatically estimated,
• The ground sampling distance of the output image is computed to fit the image resolution,
• The region of interest (upper-left corner and size of the image) is estimated so as to contain the whole input image extent.

In order to use a Digital Elevation Model (see Digital Elevation Model management.) for better localisation performances, one can pass the directory containing the DEM tiles to the application:

otbcli_OrthoRectification -io.in input_image
                          -io.out output_image
                          -elev.dem dem_dir

If one wants to use a different map projection, the -map option may be used (example with lambert93 map projection):

otbcli_OrthoRectification -io.in input_image
                          -io.out output_image
                          -elev.dem dem_dir
                          -map lambert93

Map projections handled by the application are the following (please note that the ellipsoid is always WGS84):

• UTM : -map utm | The UTM zone and hemisphere can be set by the options -map.utm.zone and -map.utm.northhem.

• Lambert 2 etendu: -map lambert2

• Lambert 93: -map lambert93

• TransMercator: -map transmercator | The related parameters (false easting, false northing and scale factor) can be set by the options -map.transmercator.falseeasting, -map.transmercator.falsenorthing and -map.transmercator.scale

• WGS : -map wgs

• Any map projection system with an EPSG code : -map epsg | The EPSG code is set with the option -map.epsg.code

The group outputs contains parameters to set the origin, size and spacing of the output image. For instance, the ground spacing can be specified as follows:

otbcli_OrthoRectification -io.in input_image
                          -io.out output_image
                          -elev.dem dem_dir
                          -map lambert93
                          -outputs.spacingx spx
                          -outputs.spacingy spy

Please note that since the y axis of the image is bottom oriented, the y spacing should be negative to avoid switching north and south direction.

A user-defined region of interest to ortho-rectify can be specified as follows:

otbcli_OrthoRectification -io.in input_image
                          -io.out output_image
                          -elev.dem dem_dir
                          -map lambert93
                          -outputs.spacingx spx
                          -outputs.spacingy spy
                          -outputs.ulx ul_x_coord
                          -outputs.uly ul_y_coord
                          -outputs.sizex x_size
                          -outputs.sizey y_size

Where the -outputs.ulx and -outputs.uly options allow to specify the coordinates of the upper-left corner of the output image. The -outputs.sizex and -outputs.sizey options allow to specify the size of the output image.

A few more interesting options are available:

• The -opt.rpc option allows to use an estimated RPC model instead of the rigorous SPOT5 model, which speeds-up the processing,
• The -opt.gridspacing option allows to define the spacing of the localisation grid used for ortho-rectification. A coarser grid results in speeding-up the processing, but with potential loss of accuracy. A standard value would be 10 times the ground spacing of the output image.
• The -interpolator option allows to change the interpolation algorithm between nearest neighbor, linear and bicubic. Default is nearest neighbor interpolation, but bicubic should be fine in most cases.
• The -opt.ram option allows to specify the amount of memory available for the processing (in Mb). Default is 256 Mb. Increasing this value to fit the available memory on your computer might speed-up the processing.