https://github.com/haoliangyu/pymasker
generate masks from Landsat and MODIS land product QA band
https://github.com/haoliangyu/pymasker
landsat mask modis-land-products remote-sensing
Last synced: about 2 months ago
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generate masks from Landsat and MODIS land product QA band
- Host: GitHub
- URL: https://github.com/haoliangyu/pymasker
- Owner: haoliangyu
- License: mit
- Created: 2015-01-16T18:34:20.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2017-07-10T04:48:10.000Z (about 8 years ago)
- Last Synced: 2025-03-18T04:12:32.513Z (7 months ago)
- Topics: landsat, mask, modis-land-products, remote-sensing
- Language: Python
- Homepage:
- Size: 44.9 KB
- Stars: 42
- Watchers: 8
- Forks: 15
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# pymasker
Pymasker is a python package to generate various masks from the Landsat Quality Assessment band and MODIS land products.
## Installation
The package can be shipped to your computer using pip.
pip install pymasker
Or just install it with the source code.
python setup.py install
This package depends on [numpy](http://www.numpy.org/) and [GDAL](https://pypi.python.org/pypi/GDAL/).
An ArcMap python toolbox based on this package could be find [here](https://github.com/haoliangyu/arcmasker).
## Use Example
### Python
For Landsat [pre-collection](https://landsat.usgs.gov/qualityband) and [collection-1](https://landsat.usgs.gov/collectionqualityband) Quality Accessment band
``` python
from pymasker import LandsatMasker
from pymasker import LandsatConfidence
# load the QA band directly
#
# The "collection" parameter is required for landsat to specify the collection
# number. Acceptable number: 0 (pre-collection), 1 (collection-1)
#
masker = LandsatMasker('LC80170302014272LGN00_BQA.TIF', collection=0)
# algorithm has high confidence that this condition exists
# (67-100 percent confidence)
conf = LandsatConfidence.high
# Get mask indicating cloud pixels with high confidence
mask = masker.get_cloud_mask(conf)
# save the result
masker.save_tif(mask, 'result.tif')
```
For MODIS land products
``` python
from pymasker import ModisMasker
from pymasker import ModisQuality
# load the QA band directly
masker = ModisMasker('MOD09GQ.A2015025.h12v04.005.2015027064556.hdf')
# Corrected product produced at ideal quality for all bands.
quality = ModisQuality.high
# Create a MODIS QA masker
mask = masker.get_qa_mask(quality)
# save the result
masker.save_tif(mask, 'result.tif')
```
### Command Line
``` bash
pymasker [source] [input.tif] [output.tif] [options...]
```
Required arguments:
```
source SOURCE
source type: landsat, modis
input INPUT
input image file path
output OUTPUT
output raster path
```
Landsat arguments:
```
-C, --collection LANDSAT COLLECTION NUMBER
collection number of input image: 0 (pre-collection), 1
-c, --confidence CONFIDENCE
level of confidence that a condition exists in a landsat image:
high, medium, low, undefined, none
-cv, --confidence_value CONFIDENCE VALUE
confidence values: -1, 0, 1, 2, 3
-m, --mask MASK
pre-collection mask: fill, cloud, cirrus, water, snow
collection-1 mask: fill, no_cloud, cloud, cloud_shadow, cirrus, snow
```
MODIS arguments:
```
-q, --quality QUALITY
Level of data quality of MODIS land products at each pixel:
high, medium, low, low_cloud
```
## More Detail
The following two articles explains the mechanism behind the tool in detail.
* [Landsat 8 Quality Assessment band](http://haoliangyu.github.io/2015/01/18/Making-masks-with-Landsat-8-Quality-Assessment-band-using-Python/)
* [MODIS land products](http://haoliangyu.github.io/2015/02/19/Making-masks-from-Quality-Control-bits-of-MODIS-land-products-in-Python-Update/)
## For JavaScript Developer
[node-qa-masker](https://github.com/haoliangyu/node-qa-masker) provides the same masking functionality in NodeJS.