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Deep feature enhancement method for land cover with irregular and sparse spatial distribution features: a case study on open-pit mining

Gaodian Zhou, Jiahui Xu, Weitao Chen, Xianju Li, Jun Li and Lizhe Wang,Deep feature enhancement method for land cover with irregular and sparse spatial distribution features: a case study on open-pit mining(IEEE TGRS, 2023)

image Framework-EG-UNet

image Edge Enhancement Moudle

image GCN

quick start

requirements

python 3.6 CUDA10.1 GPU:2080Ti*1

pytorch==1.1.0 tqdm==4.49.0 Pillow==6.1.0 opencv-python==4.1.0.25 GDAL==3.0.4

parameters

You can change epoch，batch_size，lr and decay in train_config.json

train

1.Download the files mentioned in "newdata/readme"
2.python3 main.py

It will create a folder, named 'logs', and a log file. This log file will record the training process.

And the trained model, with maximum OA in validation set, will be saved in a folder, named 'saved', and record the epoch num in 'best_epoch.txt' when saving the model.

eval

1.python3 eval.py

It will evalute this model in test dataset, and print the metrics, including OA, IOU, precision, recall, F1, then save the confusion matrix in 'saved' folder.

perdict

1.python3 predict.py

You can find the visual results in 'predict/'.

cite

@ARTICLE{10034775,
author={Zhou, Gaodian and Xu, Jiahui and Chen, Weitao and Li, Xianju and Li, Jun and Wang, Lizhe},
journal={IEEE Transactions on Geoscience and Remote Sensing}, 
title={Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining}, 
year={2023},
volume={61},
number={},
pages={1-20},
doi={10.1109/TGRS.2023.3241331}}