The code in this toolbox implements the "Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data".
Please kindly cite the papers if this code is useful and helpful for your research.
@article{WANG20221,
title = {Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data},
journal = {Information Fusion},
volume = {82},
pages = {1-18},
year = {2022},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2021.12.008},
url = {https://www.sciencedirect.com/science/article/pii/S156625352100258X},
author = {Xianghai Wang and Yining Feng and Ruoxi Song and Zhenhua Mu and Chuanming Song},
}
System-specific notes The code was tested in the environment of Python 3.7 and keras 2.3.1
How to use it? Directly run main_c.py to reproduce the results.
If you want to run the code in your own data, you can accordingly change the input (e.g., data) and tune the parameters
If you encounter the bugs while using this code, please do not hesitate to contact us. (SYFyn@outlook.com)