Fig.1 - Search Space.

Fig.2 - Search Algorithm.

1. Install Pytorch 1.9 with Python 3.8.
2. Clone this repo.

git clone https://github.com/DotWang/HKNAS.git

3. For 3-D HK-CLS and 3-D HK-SEG, setting the 3-D convolution form in main.py
4. Search, Training, Validation, Testing and Predicion (Taka an example of WHU-Hi-HongHu dataset):

• 1-D HK-CLS

cd 1DHKCLS
CUDA_VISIBLE_DEVICES=0 python main.py --flag 'honghu' --exp_num 10 --block_num 3 --layer_num 1

• 3-D HK-CLS

cd 3DHKCLS
CUDA_VISIBLE_DEVICES=0 python main.py --flag 'honghu' --exp_num 10 --block_num 3 --layer_num 3

• 3-D HK-SEG

cd 3DHKSEG
CUDA_VISIBLE_DEVICES=0 python main.py --flag 'honghu' --exp_num 10 --block_num 3 --layer_num 1

@ARTICLE{hknas,
  author={Wang, Di and Du, Bo and Zhang, Liangpei and Tao, Dacheng},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search}, 
  year={2023},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TNNLS.2023.3270369}}

[1] Pixel and Patch-level Hyperspectral Image Classification
    Adaptive Spectral–Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification, IEEE TGRS, 2020 | Paper | Github
    Di Wang^∗, Bo Du, Liangpei Zhang and Yonghao Xu

[2] Image-level/Patch-free Hyperspectral Image Classification
    Fully Contextual Network for Hyperspectral Scene Parsing, IEEE TGRS, 2021 | Paper | Github
    Di Wang^∗, Bo Du, and Liangpei Zhang

[3] Graph Convolution based Hyperspectral Image Classification
    Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification, IEEE TNNLS, 2023 | Paper | Github
    Di Wang^∗, Bo Du, and Liangpei Zhang

[4] ImageNet Pretraining and Transformer based Hyperspectral Image Classification
    DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification, IEEE TIP, 2023 | Paper | Github
    Di Wang^∗, Jing Zhang, Bo Du, Liangpei Zhang, and Dacheng Tao