by Yijie Zheng, Xiaoqing Wang, Yefei Luo, Hao Tian, Ziwei Chen.
Code for paper Segmentation and Edge Detection for Ionogram Automatic Scaling.
mmsegmentation 0.30.0.
A new branch based on OpenMMLab 2.x and mmseg 1.x is now available!
./
├── data
│ ├── BuildDataset.ipynb
│ └── IonoSeg
│ ├── img
│ ├── label
│ ├── mask
│ ├── rgbimg
│ ├── rgbmask
│ ├── splits0
│ │ ├── splits.ipynb
│ │ ├── test.txt
│ │ ├── train.txt
│ │ ├── train_val.txt
│ │ └── val.txt
│ └── viz
│ └── 20130401040700.png
├── finetune_MMSegv0.ipynb
├── Inference.ipynb
├── README.md
├── tools
│ └── test.py
└── work_dirs
└── se4ionogram
├── pspnet_r50_ionogram_mmseg0.py
└── pspnet_r50_ionogram_iou_3922_acc_9153.pth
The Dataset we use is available on google drive: Iono4311.rar.
For more information of dataset processing, please visit BuildDataset.ipynb.
The configuration of PSPNet is saved here.
Finetune the model by running finetune_MMSegv0.ipynb.
python tools/test.py ./work_dirs/se4ionogram/pspnet_r50_ionogram_mmseg0.py \
/home/ubuntu/mmsegmentation/work_dirs/se4ionogram/pspnet_r50_ionogram_iou_3922_acc_9153.pth \
--eval mIoU
Get the ionospheric parameters by running the notebook Inference.ipynb.
Method | Background Weight | Download | mTPR | DH | DF | dfoF2 |
dhF2 |
---|---|---|---|---|---|---|---|
PSPNet | 0.10 | model | 0.8713 | 4.38 | 0.12 | 98.6 | 97.0 |
PSPNet | 0.15 | model | 0.8814 | 4.63 | 0.112 | 98.3 | 97.0 |
PSPNet | 0.20 | model | 0.8070 | 4.69 | 0.100 | 98.5 | 97.8 |
PSPNet+Canny | 0.08 | model | 0.9415 | 3.05 | 0.100 | 97.7 | 98.6 |
PSPNet+Canny | 0.02 | model | 0.9256 | 2.88 | 0.091 | 98.4 | 98.8 |
PSPNet+Canny | 0.05 | model | 0.9021 | 2.82 | 0.084 | 99.1 | 98.7 |
PSPNet+Canny | 0.10 | model | 0.8713 | 3.01 | 0.08 | 99.0 | 98.5 |
PSPNet+Canny | 0.15 | model | 0.8814 | 4.63 | 0.096 | 97.9 | 98.3 |
PSPNet+Canny | 0.20 | model | 0.8070 | 4.05 | 0.093 | 98.3 | 97.1 |
If you find our work useful for your research, please consider citing:
@INPROCEEDINGS{9955166,
author={Zheng, Yijie and Wang, Xiaoqing and Luo, Yefei and Tian, Hao and Chen, Ziwei},
booktitle={2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)},
title={Segmentation and Edge Detection for Ionogram Automatic Scaling},
year={2022},
pages={115-120},
doi={10.1109/MLCCIM55934.2022.00026}
}
Should you have any questions, please send email to [email protected].