This repository contains scratch code for the paper Radargrams as Sequences, which was presented at IGARSS 2024, Athens, Greece.
The code contains a propotype of the presented work and readers could find it helpful to obtain a grasp of the idea behind the paper.
If you wish to train the main model, double check the hard-coded paths of dataset and model and run:
python main.py
To run inference, use:
python test.py
For issues, inquiries or clarifications, please write to jordy.dalcorso@unitn.it.
If you use the code or find it helpful, please cite the following paper:
@INPROCEEDINGS{10641860,
author={Corso, Jordy Dal and Bruzzone, Lorenzo},
booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
title={Radargrams as Sequences: A Method for The Semantic Segmentation of Radar Sounder Data},
year={2024},
volume={},
number={},
pages={8179-8183},
keywords={Representation learning;Radar remote sensing;Visualization;Semantic segmentation;Semantics;Object segmentation;Manuals;Semantic segmentation;Radar sounder;Sequence;Label propagation;MCoRDS},
doi={10.1109/IGARSS53475.2024.10641860}}
For further readings on sequential processing of radar sounder data, refer to:
@ARTICLE{10677400,
author={Corso, Jordy Dal and Bruzzone, Lorenzo},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={An Approach to Semantic Segmentation of Radar Sounder Data Based on Unsupervised Random Walks and User-Guided Label Propagation},
year={2024},
volume={},
number={},
pages={1-1},
keywords={Radar;Semantic segmentation;Instruments;Feature extraction;Training;Measurement;Deep learning;Radar sounder;random walks;unsupervised learning;label propagation;MCoRDS;SHARAD},
doi={10.1109/TGRS.2024.3458188}}