https://github.com/mapbox/rio-pansharpen
pansharpening landsat scenes
https://github.com/mapbox/rio-pansharpen
imagery rasterio satellite
Last synced: 3 months ago
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pansharpening landsat scenes
- Host: GitHub
- URL: https://github.com/mapbox/rio-pansharpen
- Owner: mapbox
- License: mit
- Created: 2015-07-20T18:00:39.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2019-11-19T15:32:40.000Z (over 6 years ago)
- Last Synced: 2025-08-31T04:48:24.503Z (7 months ago)
- Topics: imagery, rasterio, satellite
- Language: Python
- Size: 81.4 MB
- Stars: 54
- Watchers: 100
- Forks: 23
- Open Issues: 4
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
README
==============
rio-pansharpen
==============
.. image:: https://img.shields.io/pypi/v/rio-pansharpen.svg
:target: https://img.shields.io/pypi/v/rio-pansharpen.svg
.. image:: https://circleci.com/gh/mapbox/rio-pansharpen.svg?style=shield&circle
:target: https://circleci.com/gh/mapbox/rio-pansharpen
pansharpens Landsat 8 scenes.
What is pansharpening?
======================
Pansharpening is a process of using the spatial information in the high-resolution grayscale band (panchromatic, or pan-band) and color information in the multispectral bands to create a single high-resolution color image.
::
P pan-pixel cluster + M single multispectral pixel = M pan-sharpened pixel
Find more examples and information in the `Mapbox pansharpening blog post `_.
Install
=======
We highly recommend installing in a virtualenv. Once activated,
::
pip install -U pip
pip install rio-pansharpen
Or install from source
::
git clone https://github.com/mapbox/rio-pansharpen.git
cd rio-pansharpen
pip install -U pip
pip install -r requirements.txt
pip install -e .
Python API
==========
pansharpen.worker
-----------------
The ``worker`` module pansharpens Landsat 8. Visit the `USGS Landsat page `_ page for more information on Landsat 8 band designations.
1. ``worker.pansharpen``
------------------------
The ``worker.pansharpen`` function accepts the following as inputs:
- numpy 3D array with shape == (3, vis_height, vis_width)
- affine transform defining the georeferencing of the vis array
- numpy 2D array with shape == (pan_height, pan_width)
- affine transform defining the georeferencing of the pan array
- pansharpening method
and outputs:
- numpy 3D array with shape == (3, pan_height, pan_width)
::
>>> from pansharpen import worker
>>> from pansharpen.methods import Brovey
...
>>> pansharpened = worker.pansharpen(vis, vis_transform, pan, pan_transform,
pan_dtype, r_crs, dst_crs, weight,
method="Brovey", src_nodata=0)
2. ``worker.calculate_landsat_pansharpen``
------------------------------------------
::
>>> from pansharpen import worker
>>> from pansharpen.utils import _calc_windows
>>> import riomucho
...
>>> worker.calculate_landsat_pansharpen(src_paths, dst_path, dst_dtype,
weight, verbosity, jobs, half_window,
customwindow)
CLI
===
pansharpen
----------
::
Usage: rio pansharpen [OPTIONS] [SRC_PATHS]... DST_PATH
Pansharpens a landsat scene. Input is a panchromatic band (B8), plus 3 color
bands (B4, B3, B2)
rio pansharpen B8.tif B4.tif B3.tif B2.tif out.tif
Or with shell expansion
rio pansharpen LC80410332015283LGN00_B{8,4,3,2}.tif out.tif
Options:
--dst-dtype [uint16|uint8]
-w, --weight FLOAT Weight of blue band [default = 0.2]
-v, --verbosity
-j, --jobs INTEGER Number of processes [default = 1]
--half-window Use a half window assuming pan in aligned with
rgb bands, default: False
-c, --customwindow INTEGER Specify blocksize for custom windows >
150[default=src_blockswindows]
--help Show this message and exit.
--help Show this message and exit.
Comparison of Different Pansharpening Methods
---------------------------------------------
We've implemented the Weighted Brovey Transform for pansharpening, which is appropriate for data like Landsat where the panchromatic band is relatively similar in resolution to the color bands.
For more information on other pansharpening methods such as IHS, PCA, P+XS, Wavelet, VWP, Wavelet with Canny Edge Detector etc, please read our notes `here `_.