metadata

license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
  - remote sensing
  - EMIT
  - Hyperspectral
  - AVIRIS
  - methane
  - CH4

STARCOP pre-trained models

This repository contains the trained models of the publication:

V. Růžička, G. Mateo-Garcia, L. Gómez-Chova, A. Vaughan, L. Guanter, and A. Markham. Semantic segmentation of methane plumes with hyperspectral machine learning models. Scientific Reports 13, 19999 (2023). DOI: 10.1038/s41598-023-44918-6.

We include the trained models:

HyperSTARCOP, only mag1c in folder models/hyperstarcop_mag1c_only
HyperSTARCOP, mag1c + rgb in folder models/hyperstarcop_mag1c_rgb

The following table shows the performance of the models in the AVIRIS test dataset and in the EMIT test dataset:

In order to run any of these models:

In a EMIT scene see the tutorial Run STARCOP models on raw EMIT data.

If you find this work useful please cite:

@article{ruzicka_starcop_2023,
    title = {Semantic segmentation of methane plumes with hyperspectral machine learning models},
    volume = {13},
    issn = {2045-2322},
    url = {https://www.nature.com/articles/s41598-023-44918-6},
    doi = {10.1038/s41598-023-44918-6},
    number = {1},
    journal = {Scientific Reports},
    author = {Růžička, Vít and Mateo-Garcia, Gonzalo and Gómez-Chova, Luis and Vaughan, Anna, and Guanter, Luis and Markham, Andrew},
    month = nov,
    year = {2023},
    pages = {19999}
}