.. _pansharpen:

Pansharpening
=============

Landsat includes a **panchromatic band** which has greater spatial resolution
(15 m versus the standard 30 m of visible bands).
It can be used to artificially increase the resolution of the visible bands
(red, green, and blue) in a process called **pansharpening**.

To illustrate this concept, let's download a scene from just North of the city
of Liverpool, UK:

.. jupyter-execute::

    import xlandsat as xls
    import matplotlib.pyplot as plt

    path = xls.datasets.fetch_liverpool()
    path_pan = xls.datasets.fetch_liverpool_panchromatic()

Load the scene with :func:`xlandsat.load_scene`:

.. jupyter-execute::

    scene = xls.load_scene(path)
    scene


And load the panchromatic band with :func:`xlandsat.load_panchromatic`:

.. jupyter-execute::

    panchromatic = xls.load_panchromatic(path_pan)
    panchromatic

Now we can plot an RGB composite and the panchromatic band for comparison:

.. jupyter-execute::

    rgb = xls.composite(scene, rescale_to=(0, 0.25))

    plt.figure(figsize=(16, 10))
    ax = plt.subplot(2, 1, 1)
    rgb.plot.imshow(ax=ax)
    ax.set_aspect("equal")
    ax.set_title("RGB")
    ax = plt.subplot(2, 1, 2)
    panchromatic.plot.pcolormesh(
        ax=ax, cmap="gray", vmin=0.02, vmax=0.1, add_colorbar=False,
    )
    ax.set_aspect("equal")
    ax.set_title("Panchromatic")
    plt.tight_layout()

The pansharpening is implemented in :func:`xlandsat.pansharpen`:

.. jupyter-execute::

    scene_sharp = xls.pansharpen(scene, panchromatic)
    scene_sharp

Finally, let's compare the sharpened and original RGB composites:

.. jupyter-execute::

    rgb_sharp = xls.composite(scene_sharp, rescale_to=(0, 0.15))

    plt.figure(figsize=(16, 10))
    ax = plt.subplot(2, 1, 1)
    rgb.plot.imshow(ax=ax)
    ax.set_aspect("equal")
    ax.set_title("Original")
    ax = plt.subplot(2, 1, 2)
    rgb_sharp.plot.imshow(ax=ax)
    ax.set_aspect("equal")
    ax.set_title("Pansharpened")
    plt.tight_layout()