https://github.com/caseyyoungflesh/ts-norm
Time series normalization for satellite imagery
Science Score: 13.0%
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Time series normalization for satellite imagery
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- Host: GitHub
- Owner: caseyyoungflesh
- Default Branch: master
- Size: 3.36 MB
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Fork of latmperkmol/ts-norm
Created almost 5 years ago
· Last pushed about 6 years ago
https://github.com/caseyyoungflesh/ts-norm/blob/master/
# ts-norm Time series normalization for satellite imagery. The paper corresponding to this work is published in the Journal of Computers and Electronics in Agriculture and [can be found here.](https://doi.org/10.1016/j.compag.2019.104893) ## Installation ts-norm runs as a script, primarily using functions out of custom_utils.py. To "install", simply download the repo. Creating an anaconda environment is highly recommended. ``` conda create --name tsnorm conda activate tsnorm conda install numpy scipy matplotlib seaborn scikit-image conda install gdal conda install geopandas rasterio conda install -c conda-forge basemap pykridge # optional, used for some arosics functions pip install arosics # also optional, used for image co-registration ``` ## Usage Currently, only the python interface is supported, but a CLI will be implemented. Executing `custom_utils.main` will normalize a target image to a reference image. Note that a substantial number of intermediate products are currently written to the disk during this process, so making a new folder for your outputs is advisable. A Jupyter Notebook demonstrating a multi-sensor application is included. That is the best way to get familiar with the script!
Owner
- Name: Casey Youngflesh
- Login: caseyyoungflesh
- Kind: user
- Company: Michigan State University
- Website: www.caseyyoungflesh.com
- Twitter: caseyyoungflesh
- Repositories: 2
- Profile: https://github.com/caseyyoungflesh
Quantitative Ecology | Global Change | Population Biology | Biodiversity