Deep residual networks for hyperspectral image classification

@article{Zhong2017DeepRN,
  title={Deep residual networks for hyperspectral image classification},
  author={Zilong Zhong and Jonathan Li and Lingfei Ma and Han Jiang and He Zhao},
  journal={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2017},
  pages={1824-1827}
}
Deep neural networks can learn deep feature representation for hyperspectral image (HSI) interpretation and achieve high classification accuracy in different datasets. However, counterintuitively, the classification performance of deep learning models degrades as their depth increases. Therefore, we add identity mappings to convolutional neural networks for every two convolutional layers to build deep residual networks (ResNets). To study the influence of deep learning model size on HSI… CONTINUE READING