Locality-Aware Hyperspectral Classification

The code implements the "Locality-Aware Hyperspectral Classification"

Example of training

$python main.py --dataset='Indian' --epoches=300 --patches=7 --band_patches=1 --mode='CAF' --weight_decay=5e-3 --flag='train' --output_dir='./logs/' --batch_size=32 --align='align' --spatial_attn

Example of visualizing the results

$python visualization.py

Citations:

The code is built upon SpectralFormer and MAEST, thanks to their great work! If you find it is useful for your research, please kindly cite the following papers:

• Zhou et al. (2023) - Locality-Aware Hyperspectral Classification
• Hong et al. (2021). - SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers
• Damian et al. (2022) - Masked Auto-Encoding Spectral–Spatial Transformer for Hyperspectral Image Classification