AM³Net

We have relased the code of Adaptive Mutual-learning-based Multimodal Data Fusion Network (AM³Net) algorithm. And the paper has been published in IEEE TCSVT 2022. In this paper, we conducted the experiments on the hyperspectral and lidar dataset(Houston and Trento) and multispectral and synthetic aperture radar data (grss-dfc-2007 datasets).

If you have any queries, please do not hesitate to contact me (jinping_wang@foxmail.com).

Requirements：

• We have tested our algorithm in the following on Windows with CUDA=11.0.

  torch==1.7.0+cu110
  visdom==0.1.8
  numpy==1.19.5
  scipy==1.5.4
  sklearn=0.24.2
  random
  mmcv==1.3.0
  cupy-cuda110==8.5.0
  

• mmcn is provided by open-mmlab [https://github.com/open-mmlab/mmcv]: python setup.py install

Train：

• If you want to run on other dataset, conduct the data.mat.

• Trento Data (Hyperspectral and LiDAR Data): Trento dataset is provided by Professor Prof. L. Bruzzone from the University of Trento.

  data.mat  [C1 >> C2]
  ----> ground (H*W)
  ----> HSI_data (H*W*C1)
  ----> Lidar_data (H*W*C2)
  

• Start a Visdom server: python -m visdom.server and go to http://localhost:8097 to see the visualizations.

Test：

• You can use the pre-trained models (model_Trento.pt, model_Houston.pt, and model_GRSS07.pt) to obtain the classification results!

Citation：

• Please cite us if our project is helpful to you!

J. Wang, J. Li, Y. Shi, J. Lai and X. Tan, "AM3Net: Adaptive Mutual-learning-based Multimodal Data Fusion Network," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5411-5426, Aug. 2022, doi: 10.1109/TCSVT.2022.3148257.

Bibtex format :

  @ARTICLE{9698196,
  author={Wang, Jinping and Li, Jun and Shi, Yanli and Lai, Jianhuang and Tan, Xiaojun},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={AM$3$Net: Adaptive Mutual-learning-based Multimodal Data Fusion Network}, 
  year={2022},
  volume={32},
  number={8},
  pages={5411-5426},
  doi={10.1109/TCSVT.2022.3148257}}