Commit ebeeecea authored by Shanyu Zhou's avatar Shanyu Zhou
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parent dca5776f
# wv3plastic
# wvpd
Tool for automatically detection of aliphatic and aromatic plastic on the configuration of WorldView-3 imagery
## Requirements
This tool is compatible with Python 3.8.11.
Dependencies can be installed using [pip](
pip install -r requirements.txt
## Test data
The test folder contains data that can be used to test the processing chain. It contains one PRISMA L2D image, showing parts of the Almeria, Spain.
## Quickstart
To start training a classification right away, run the example script:
cd /path of
## Usage
This tool is tested with 'Bottom-of-atmosphere' reflectance, please make sure the atmospheric correction has been accomplished before apply it
It allows batch processing when there are several dataset in the target folder
It can be modified as below:
# Get the path of
bpath = os.getcwd()
# Input can be hyperspectral data, e.g. the example data was recorded by PRISMA
# Path here could be replaced by other paths containing your own dataset
# Must add '/' in the end of the string
path =bpath+'/example/'
# This line is only used on hyperspectral data for spectral resampling to WV3 configuration
# Please mute this line if directly use WV-3 dataset
path = multispecim(path)
# This line is for the classication using default setting in the paper
# Please change the setting in the function on demands
## License
working on
## Citation
The procedure has been used for the publication of the following paper.
Zhou, S., Kuester, T., Bochow, M., Bohn, N., Brell, M., Kaufmann, H., 2021. A knowledge-based, validated classifier for the identification of aliphatic and aromatic plastics by WorldView-3 satellite data. Remote Sensing of Environment 264, 112598.
If you use it for processing your own data, please cite my article when publishing your findings, thank you.
Plastic detection by WV3
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