Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great
success. However, the deep neural network model has a large parameter space and requires a large number of labeled data.
Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast patch-free
global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information.
However, FPGA has difficulty extracting the most discriminative features when the sample data is imbalanced. In this paper,
a spectral-spatial dependent global learning (SSDGL) framework based on global convolutional long short-term memory (GCL)
and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the
hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample
problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to
extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the
GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show
that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.
- This is an official implementation of SSDGL in our paper "A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification".
[1] Q. Zhu, W. Deng, Z. Zheng, Y. Zhong, Q. Guan, W. Lin, L. Zhang, and D. Li,
“A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification”,
IEEE Trans. Cybern., DOI:10.1109/TCYB.2021.3070577.
- Please cite the following reference:
- Q. Zhu, W. Deng, Z. Zheng, Y. Zhong, Q. Guan, W. Lin, L. Zhang, and D. Li, “A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification”, IEEE Trans. Cybern., DOI:10.1109/TCYB.2021.3070577.
- You can contact the e-mail [email protected] if you have further questions about the usage of codes and datasets.
- For any possible research collaboration, please contact Prof. Qiqi Zhu ([email protected]).
pytorch >= 1.1.0
tensorboardX
opencv, skimage, sklearn, pillow, SimpleCV
SimpleCV install:
pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git
or
download folder from https://github.com/Z-Zheng/SimpleCV.git and run python setup.py install
image_mat_path='./pavia/PaviaU.mat'
gt_mat_path='./pavia/PaviaU_gt.mat'image_mat_path='./salinas/Salinas_corrected.mat',
gt_mat_path='./salinas/Salinas_gt.mat',image_mat_path='./IndianPines/Indian_pines_corrected.mat',
gt_mat_path='./IndianPines/Indian_pines_gt.mat',./module/SSDGL.py Need to adjust the number of categories
bash scripts/SSDGL_1_0_pavia.sh
./module/SSDGL.py Need to adjust the number of categories
bash scripts/SSDGL_1_0_salinas.sh
./module/SSDGL.py Need to adjust the number of categories
bash scripts/SSDGL_1_0_indianpine.sh
./module/SSDGL.py Need to adjust the number of categories
bash scripts/SSDGL_1_0_HOS.sh- If you extend or use this work, please cite the paper where it was introduced:
@ARTICLE{9440852,
author={Zhu, Qiqi and Deng, Weihuan and Zheng, Zhuo and Zhong, Yanfei and Guan, Qingfeng and Lin, Weihua and Zhang, Liangpei and Li, Deren},
journal={IEEE Transactions on Cybernetics},
title={A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification},
year={2021},
volume={},
number={},
pages={1-15},
doi={10.1109/TCYB.2021.3070577}}
GRSS2013_HOS datasets
Baidu Drive
Link: https://pan.baidu.com/s/1kPF5f857cJHH617TluOLqQ Code: a3qc