Pymasker is a python package to generate various masks from the Landsat Quality Assessment band and MODIS land products.
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 and GDAL.
An ArcMap python toolbox based on this package could be find here.
For Landsat pre-collection and collection-1 Quality Accessment band
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
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')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
The following two articles explains the mechanism behind the tool in detail.
node-qa-masker provides the same masking functionality in NodeJS.
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