HyperspectralImageClassification_CACFTNet_TGRS2024

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 (https://github.com/cslxju/HyperspectralImageClassification_CACFTNet_TGRS2024)

https://ieeexplore.ieee.org/document/10460571

Requirements

-PyTorch 1.6

-CUDA 10.1

-Python 3.7

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}}

Citation