A Cross-level Spectral-Spatial Joint Encoding Learning Framework for Imbalanced Hyperspectral Image Classification

Accpetd by IEEE Transactions on Geoscience and Remote Sensing

Table of Contents

• Background
• test
• test_images
• Citation
• Acknowledgement

Background

Hyperspectral Image Classification

test

install simpleCV

$ pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git

$ python train.py

Citation

[1]Zheng Z, Zhong Y, Ma A, et al. FPGA: Fast patch-free global learning framework for fully end-to-end hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(8): 5612-5626.

[2]Zhu Q, Deng W, Zheng Z, et al. A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification[J]. IEEE Transactions on Cybernetics, 2021.

Acknowledgement

This code is built on SSDGL and FPGA (PyTorch). We thank the authors for sharing their codes of Freenet.

Cite our work

@ARTICLE{9875335,
  author={Yu, Dabing and Li, Qingwu and Wang, Xiaolin and Xu, Chang and Zhou, Yaqin},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={A Cross-Level Spectral–Spatial Joint Encode Learning Framework for Imbalanced Hyperspectral Image Classification}, 
  year={2022},
  volume={60},
  number={},
  pages={1-17},
  doi={10.1109/TGRS.2022.3203980}}