pyDMS

Python implementation of Data Mining Sharpener (DMS): a decision tree based algorithm for sharpening (disaggregation) of low-resolution images using high-resolution images. The implementation is mostly based on [Gao2012].

The DMS is trained with high-resolution data resampled to low resolution and low-resolution data and then applied directly to high-resolution data to obtain high-resolution representation of the low-resolution data.

The implementation includes selecting training data based on homogeneity statistics and using the homogeneity as weight factor ([Gao2012], section 2.2), performing linear regression with samples located within each regression tree leaf node ([Gao2012], section 2.1), using an ensemble of regression trees ([Gao2012], section 2.1), performing local (moving window) and global regressions and combining them based on residuals ([Gao2012] section 2.3) and performing residual analysis and bias correction ([Gao2012], section 2.4)

Additionally, the Decision Tree regressor can be replaced by Neural Network regressor.

Installation

python_dms requires GDAL to be installed on your system. Since GDAL provides compiled binaries that vary by platform, this package does not bundle them. You will need to install GDAL yourself before installing python_dms.

1. Install GDAL

   macOS (Homebrew)

   brew install gdal
   

   Ubuntu / Debian

   sudo apt-get update
   sudo apt-get install -y gdal-bin libgdal-dev
   

   Windows

   Download and install GDAL from GIS Internals or install via conda:

   conda install -c conda-forge gdal
   

   You can verify your installation by running:

   gdalinfo --version
   

2. Install python_dms

   Once GDAL is installed and available in your environment, install python_dms with pip:

   pip install python_dms
   

   or download the project to your local system, enter the download directory and then type

   python setup.py install

Usage

For usage template see run_pyDMS.py.

Copyright: (C) 2024 Radoslaw Guzinski and contributors.

References

• [Gao2012] Gao, F., Kustas, W. P., & Anderson, M. C. (2012). A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sensing, 4(11), 3287–3319. https://doi.org/10.3390/rs4113287

• [Guzinski2019] Guzinski, R., & Nieto, H. (2019). Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sensing of Environment, 221, 157–172. https://doi.org/10.1016/j.rse.2018.11.019

• [Guzinski2023] Guzinski, R., Nieto, H., Ramo Sánchez, R., Sánchez, J.M., Jomaa, I., Zitouna-Chebbi, R., Roupsard, O., and López-Urrea, R. (2023). Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion. International Journal of Applied Earth Observation and Geoinformation 125, 103587. https://doi.org/10.1016/j.jag.2023.103587

License

pyDMS: a Python Data Mining Sharpener implementation

Copyright 2024 Radoslaw Guzinski and contributors.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.