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From sensor data to DSM : the step-by-step tutorials
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Use CARS with Pleiades images ...
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.. note::
	CARS is used in the same way with Pléiades and Pléiades Neo data.

.. _pleiade_raw_data:

... with raw data
-----------------


If you want to generate a 3D model with the following pair:

.. code-block:: bash

    IMG_PHR1B_MS_003
    IMG_PHR1B_MS_004
    IMG_PHR1B_P_001
    IMG_PHR1B_P_002

You should find in each folder the following data:

.. code-block:: bash

    ...
    DIM_PHR1B_***.XML
    IMG_PHR1B_***.TIF
    RPC_PHR1B_***.XML


For each product, the user must provide the path to the pancromatic data (*P*.TIF) with its geomodel, all contained in the DIMAP file (DIMAP*P*.XML):


.. code-block:: json

    {
    "inputs": {
        "sensors" : {
            "one": {
                "image": "IMG_PHR1B_P_001/DIM_PHR1B_***.XML"
            },
            "two": {
                "image": "IMG_PHR1B_P_002/DIM_PHR1B_***.XML",
            }
        },
        "pairing": [["one", "two"]]
        }
    }



If you want to add the colors, a P+XS fusion must be done, to specify a color.tif with the same shape and resolution than the Pancromatic data.
It can be performed with `otbcli_BundleToPerfectSensor` as explained in  :ref:`make_a_simple_pan_sharpening`.

.. code-block:: json

    {
    "inputs": {
        "sensors" : {
            "one": {
                "image": "IMG_PHR1B_P_001/DIM_PHR1B_***.XML",
                "color": "color_one.tif"
            },
            "two": {
                "image": "IMG_PHR1B_P_002/DIM_PHR1B_***.XML",
                "color": "color_two.tif"

            }
        },
        "pairing": [["one", "two"]]
        }
    }




.. _pleiade_roi_data:

... with a region of interest
-----------------------------

There are two different ways to use a ROI in CARS:

* Crop input images: the whole pipeline will be done with cropped images
* Use input roi parameter: the whole images will be used to compute grid correction and terrain + epipolar a priori. Then the rest of the pipeline will use the given roi. This allows a better correction of epipolar rectification grids.


If you want to work with cropped image by using a region of interest for the whole pipeline, use cars-extractroi:

.. code-block:: bash

    cars-extractroi -il DIM_PHR1B_***.XML -out ext_dir -bbx -58.5896 -34.4872 -58.5818 -34.4943

It generates a .tif and .geom to be used as:

.. code-block:: json

    {
    "inputs": {
        "sensors" : {
            "one": {
                "image": "ext_dir/***.tif",
                "geomodel": "ext_dir/***.geom",
                "color": "color_one.tif"
            }
    }

Then use the generated data as you would with raw data.


If you want to compute the grid correction and compute the epipolar/terrain a priori on the whole image, keep the same input images but specify the terrain ROI to use:

.. code-block:: json

    {
        "inputs":
        {
            "roi" : {
                "type": "FeatureCollection",
                "features": [
                    {
                    "type": "Feature",
                    "properties": {},
                    "geometry": {
                        "coordinates": [
                        [
                            [5.194, 44.2064],
                            [5.194, 44.2059],
                            [5.195, 44.2059],
                            [5.195, 44.2064],
                            [5.194, 44.2064]
                        ]
                        ],
                        "type": "Polygon"
                    }
                    }
                ]
            }
        }
    }



See  Usage Sensors Images Inputs configuration for more information.

.. note::
	CARS also works with other types of data: SPOT 6-7, WorldView, etc.