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README.txt
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## Requirements
-PyTorch 1.6
-CUDA 10.1
-Python 3.7
##Code Run
-------------------------------Train--------------------------------------------------------------------------------------------------------------------------
Indian Pines ：
`python demo.py --dataset='Indian' --epoches=1500 --patches=7 --band_patches=1 --mode='CAF' --weight_decay=5e-3 --channels_band=200`
Pavia University:
`python demo.py --dataset='Pavia' --epoches=1680 --patches=7 --band_patches=1 --mode='CAF' --weight_decay=5e-3 --channels_band=103`
Houston:
`python demo.py --dataset='Houston' --epoches=1500 --patches=7 --band_patches=1 --mode='CAF' --weight_decay=5e-3 --channels_band=144`
-------------------------------Train--------------------------------------------------------------------------------------------------------------------------
-------------------------------Test--------------------------------------------------------------------------------------------------------------------------
Indian Pines：
`python demo.py --dataset='Indian' --flag_test=test --patches=7 --band_patches=1 --mode='CAF' --channels_band=200`
Pavia University:
`python demo.py --dataset='Pavia' --flag_test=test --patches=7 --band_patches=1 --mode='CAF' --channels_band=103`
Houston:
`python demo.py --dataset='Houston' --flag_test=test --patches=7 --band_patches=1 --mode='CAF' --channels_band=144`
-------------------------------Test--------------------------------------------------------------------------------------------------------------------------
## Dataset
We used three publicly available datasets, Indian Pines, Pavia University, and Houston2013. The data set can be accessed at the following link:
https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
Or it can be found in the data folder of the zip package
##Network
Because the number of channels in different data sets is different, the corresponding network structure is also different.
Indian Pines and Houston2013 datasets: vit_pytorch_indian_Houston.py
Pavia University datasets: vit_pytorch_pavia.py
## ######################################## Using the code should cite the following paper ########################################
S. Cheng, R. Chan and A. Du, "CACFTNet: A Hybrid Cov-Attention and Cross-Layer Fusion Transformer Network for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-17, 2024, doi: 10.1109/TGRS.2024.3374081.
@ARTICLE{10460571,
author={Cheng, Shuli and Chan, Runze and Du, Anyu},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={CACFTNet: A Hybrid Cov-Attention and Cross-Layer Fusion Transformer Network for Hyperspectral Image Classification},
year={2024},
volume={62},
number={},
pages={1-17},
keywords={Feature extraction;Convolutional neural networks;Transformers;Data mining;Convolution;Image classification;Task analysis;Covariance;cross-layer attention;feature fusion;hyperspectral (HS) image classification;transformer},
doi={10.1109/TGRS.2024.3374081}}
###
If our work has been useful to you, please cite our work in your article.