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SC-EADNet

The Official PyTorch implementation of SC-EADNet on TGRS 2022 paper SC-EADNet: A Self-Supervised Contrastive Efficient Asymmetric Dilated Network for Hyperspectral Image Classification.

Requirements

conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
pip install tqdm pandas scipy numpy

Dataset

Indian Pines, Salinas, PaviaU, Houston 2013 datasets are used in this repo, the dataset should be downloaded into dataset/HSIdata directory.

Prepare Dataset

cd dataset
python makeTrainingSet.py

Usage

Train SC-EADNet

python main.py --batch_size 128 --epochs 100 

Linear Evaluation

python linear.py --model_path /path/to/pretrained/checkpoint/

Citation

@ARTICLE{9627700,  
author={Zhu, Mingzhen and Fan, Jiayuan and Yang, Qihang and Chen, Tao},  
journal={IEEE Transactions on Geoscience and Remote Sensing},   
title={SC-EADNet: A Self-Supervised Contrastive Efficient Asymmetric Dilated Network for Hyperspectral Image Classification},   
year={2022},  
volume={60},  
number={},  
pages={1-17},  
doi={10.1109/TGRS.2021.3131152}}