Hyperspectral Image Classification With Mixed Link Networks

PyTorch implementation of hyperspectral image classification with mixed link networks.

Basic Usage

import torch
from MLNets import MLNet_A, MLNet_B

model = MLNet_A(num_classes=16, channels=200)
# model = MLNet_B(num_classes=16, channels=200)
model.eval()
print(model)
input = torch.randn(100, 200, 11, 11)
y = model(input)
print(y.size())

Paper

Hyperspectral image classification with mixed link networks

Please cite our paper if you find it useful for your research.

@article{meng2021hyperspectral,
  title={Hyperspectral image classification with mixed link networks},
  author={Meng, Zhe and Jiao, Licheng and Liang, Miaomiao and Zhao, Feng},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume={14},
  pages={2494--2507},
  year={2021},
  publisher={IEEE}
}

Acknowledgement

We thank authors of MixNet for sharing their code. We implement our MLNets based on their source code. If you find this code is helpful in your research, please consider citing our paper and MixNet