Active deep densely connected convolutional network for hyperspectral image classification

@article{Liu2021ActiveDD,
  title={Active deep densely connected convolutional network for hyperspectral image classification},
  author={Bing Liu and Anzhu Yu and Pengqiang Zhang and Lei Ding and Wenyue Guo and Kuiliang Gao and Xibing Zuo},
  journal={International Journal of Remote Sensing},
  year={2021},
  volume={42},
  pages={5915 - 5934}
}
  • Bing Liu, Anzhu Yu, Xibing Zuo
  • Published 1 September 2020
  • Computer Science, Environmental Science
  • International Journal of Remote Sensing
ABSTRACT Deep-learning-based methods have seen a massive rise in popularity for hyperspectral image classification over the past few years. However, the success of deep learning is attributed greatly to numerous labelled samples. It is still very challenging to use only a few labelled samples to train deep learning models to reach a high classification accuracy. An active deep-learning framework trained by an end-to-end manner is, therefore, proposed by this paper in order to minimize the… 

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