This is a python module for post-processing images received from NOAA satellites. In particular, this module will attempt to georeference the images.

Operations that currently seem to be working:

• Histogram stretch (global and adaptive)
• Write to a file (can write whole image, left channel only, right channel only, or both)
• Border (can draw border around the area of detected actual sat image)
• Denoise
• Show image (useful for playing with parameters)
• Georeferencing (experimental)

Author: Tomek Mrugalski

Do the standard python thing:

python3 -m venv venv source venv/bin/activate pip install -r requirements.txt 

To process an image, you should call process_img and pass two parameters. First is the name of the PNG file and the second parameter is a structure that explains what exactly you want to be done with the image. Here's a minimal code that does image extraction and enhacements:

from noaatools.imageproc import process_img params = { "histogram": True, "histogram-adaptive": True, "border": True, "show": False, "write": True, "write-left": True, "write-right": False, "denoise": False, "georef": False } process_img("tests/1276.png", params)

Currently is being worked on. There's code in noaa_postproc/georef.py, but it doesn't work yet.

Example code:

from noaatools import georef # Specify the parameters tle1 = '1 28654U 05018A 20098.54037539 .00000075 00000-0 65128-4 0 9992' tle2 = '2 28654 99.0522 154.2797 0015184 73.2195 287.0641 14.12501077766909' aos = '2020-04-12 09:01:03.063476' los = '2020-04-12 09:17:06.466954' filename = '1276.png' method = georef.Method.PYMAP3D # Call the routine to calculate the image corners. d1, d2, aos_lla, los_lla, corner_ul, corner_ur, corner_ll, corner_lr = georef.georef(method, tle1, tle2, aos, los) # You can then export the data: export_js.export2cesium(outfile_js, satname, d1, d2, aos_lla, los_lla, corner_ul, corner_ur, corner_ll, corner_lr, tle1, tle2, method.name) export_csv.export2csv(outfile_csv, satname, d1, d2, aos_lla, los_lla, corner_ul, corner_ur, corner_ll, corner_lr, tle1, tle2, method.name) 

This currently supports georeferencing only.

python -m noaatools.noaatools --help usage: noaatools [-h] [--aos AOS] [--los LOS] [--tle1 TLE1] [--tle2 TLE2] [--method METHOD] [--satname SATNAME] [--file FILE] optional arguments: -h, --help show this help message and exit --aos AOS Specify Acquision of Signal as timestamp (default: 2020-04-12Z09:01:03.063476) --los LOS Specify Loss of signal as timestamp (default: 2020-04-12Z09:17:06.466954) --tle1 TLE1 First line of TLE (default: 1 28654U 05018A 20098.54037539 .00000075 00000-0 65128-4 0 9992) --tle2 TLE2 Second line of TLE (default: 2 28654 99.0522 154.2797 0015184 73.2195 287.0641 14.12501077766909) --method METHOD Specify orbital position calculation method (SPHERICAL, OBLATE, PYMAP3D, default: PYMAP3D) --satname SATNAME Specifies satellite name (default: NOAA 18) --file FILE Specifies output file pattern (default: data/1276.png)

The tool will export data to files similar to the specified. When you specify 1276.png, the actual output files will be 1276.js and 1276.txt. By default this tool uses Svarog observation 1276 as sample data. That way you can easily run it without any parameters to quickly see how it works:

python -m noaatools.noaatools

Currently there are three calculation method implemented:

• spherical (the simplest and least accurate method. It assumes Earth is completely round)
• oblate (this accounts for Earth oblateness, i.e. the bulge around equator)
• pymap3d (this method is using PYMAP3D's eci2ecef and ecef2geodetic functions)

Currently there are two export methods implemented:

• JavaScript - this generates a JavaScript code that is using Cesium. You can see the results easily by pasting the generated file on https://sandcastle.cesium.com
• CSV - Comma Separated File. This is a very flexible text format. You can import this data into OpenOffice, Microsoft Excel or many other tools and environments

The unit-tests coverage is miserable so far. Nevertheless, you may do the following:

pip install pytest python -m pytest -s -v 

During the first run, an image for satnogs observation 1276 (about 3MB) will be downloaded. It's a reasonably recent, good quality observation. I've decided to keep the repo clean and not pollute it with unnecessary images.

• GDAL + python: https://pcjericks.github.io/py-gdalogr-cookbook/
• Georeferencing in qgis: https://www.qgistutorials.com/en/docs/georeferencing_basics.html
• pymap3d has tons of useful coordinate transforms: https://geospace-code.github.io/pymap3d/index.html
• wxtoimg man page: https://wxtoimgrestored.xyz/support/wxtoimg.pdf
• Cesium support for displaying a single image is confusing. https://www.cesium.com/docs/cesiumjs-ref-doc/SingleTileImageryProvider.html
• PROJ4 (recommended by @fivitti): https://github.com/pyproj4/pyproj
• https://github.com/Alexander-Barth/APTDecoder.jl