/VSDF

Code for VSDF: A variation-based spatiotemporal data fusion method

Primary LanguagePythonApache License 2.0Apache-2.0

VSDF: A variation-based spatiotemporal data fusion method

Python code for VSDF Available time: 23/10/2022

Requirement for Python:

  • Python 3.6
  • gdal 3.1.4
  • torch 1.10.2
  • numpy 1.19.0
  • skimage 0.17.2
  • sklearn 0.24.2
  • guided-filter-pytorch 3.7.5

Requirement for input images:

  • Format: recognized by GDAL, GeoTif is recomended
  • Size: fine image and coarse images should be in the same size (e.g., 800*800)
  • Band number: only 6 bands is tested

Input:

  1. L1: Fine image at T1
  2. M1: Coarse image at T1
  3. M2: Coarse image at T2

Output:

  • Fusion_L2 Tif

Cite

If you find VSDF is helpful, please cite the following work: VSDF [Paper] [Code]

@article{XU2022113309,
title = {VSDF: A variation-based spatiotemporal data fusion method},
journal = {Remote Sensing of Environment},
volume = {283},
pages = {113309},
year = {2022},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2022.113309},
}

NEW! FastVSDF

Speed up VSDF with 40+ times! [Paper] [Code]

@ARTICLE{10399795,
  author={Xu, Chen and Du, Xiaoping and Fan, Xiangtao and Jian, Hongdeng and Yan, Zhenzhen and Zhu, Junjie and Wang, Robert},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={FastVSDF: An Efficient Spatiotemporal Data Fusion Method for Seamless Data Cube}, 
  year={2024},
  doi={10.1109/TGRS.2024.3353758}}