majamask 1.0.0

Creates a simplified rastermask from CLM and MG2 masks

MAJAMASK

MajaMask is a small application that lets you create a simplified rastermask from two bitmasks present in Muscate products : CLM and MG2 masks. It can help to visualize key features, though decreases the amount of information initially available.

MajaMask can be used as a standalone python module or through StartMaja to compute the rastermask as a direct output.

1. Output mask description
2. MajaMask as a Standalone
3. MajaMask with StartMaja

1 - Output mask description

MajaMask generates a rastermask from CLM and MG2 masks of Muscate products. A description of those two masks can be found here. Both mask contain information stored in 8-bit images, a combination of which giving the set of detected features for each pixel. In contrast, MajaMask outputs a mask containing only 9 values, each corresponding to one feature. As a result, the output rastermask contains less information but allows quick visual interpretation.

The 9 classes of pixels are attributed following a priority order described in the following table :

Pixel value	Feature	Bit considered in input masks
1	Cirrus	CLM bit 7
2	All clouds except cirrus	CLM bit 1
3	Cloud shadows	MG2 bit 3
4	Snow	MG2 bit 2
5	Water	MG2 bit 0
6	Topological shadows	MG2 bit 4
7	Hidden areas due to terrain	MG2 bit 5
8	Tangent or low sun	MG2 bit 6 or 7
0	Valid reflectance	None of the above

MajaMask as a Standalone

Requirements

MajaMask requires that you work with python >= 3.

Additionally, MajaMask will install some dependencies on your environment. In order for majaMask to install dependencies successfully, please :

• upgrade pip to latest version using pip install --upgrade pip on your python environment (required by numba)

• install the following package on your system (required by GDAL) :

  On Debian/Ubuntu : apt get install libgdal-dev

  On ReadHat : dnf install gdal-devel

Installation

It is advised to install MajaMask in a dedicated python environment.

And then, simply use:

    pip install numpy==1.24.4 && pip install --no-cache-dir gdal==GDAL_VERSION majamask

The order is important, to get GDAL_VERSION you can execute gdalinfo --version

Use

Use MajaMask as a command line in a terminal :

rastermask -clm <path/to/CLM/tif> -mg2 <path/to/MG2/tif> -out <path/to/output/tif>

MajaMask may raise an error if one of these arguments is missing or if CLM and MG2 masks are not 8-bit images of the same size.

Alternatively, you can give the path to a L2 product and the application will automatically generate one rastermask for each couple of CLM and MG2 masks found at each resolution :

rastermask -product <path/to/L2/product> 

The generated rastermask(s) will be stored in the <product/Path>/MASKS/ folder and will be called rastermask.tif (respectively rastermask_R1.tif for R1 resolution, and so on).

Command line arguments (-clm, -mg2, -out) and (-product) are mutually exclusive, please provide one of the two options exactly.

You can also use MajaMask as a module in your own python modules, for example:

import majamask.rastermask as rastermask

You can then create a rastermask using the create_rastermask(input_mask_clm, input_mask_mg2, output_raster) function. It can be combined with the find_inputs(product_path) function to automatically find and process all CLM and MG2 masks present in a L2 product :

input_array = rastermask.find_inputs(product_folder)
for clm, mg2, out in input_array :
    rastermask.create_rastermask(clm, mg2, out)

MajaMask with StartMaja

MajaMask module is pre-installed in StartMaja. Details to install MAJA and use StartMaja can be found here.

MajaMask can be used in StartMaja by setting the option --rastermask in the StartMaja command line. This will generate the rastermask for all output L2A images. The rastermask will be stored in the same directory as the CLM and MG2 masks (<product/Path>/MASKS/) and called rastermask.tif. One rastermask will be generated for each resolution (rastermask_R1.tif, rastermask_R2.tif, ...)